clusters stringlengths 2 332 | sentences stringlengths 265 3.01k | ner stringlengths 16 1.27k | relations stringlengths 16 1.06k | doc_key stringlengths 8 24 | split stringclasses 3 values | ner_re_processed stringlengths 618 8.5k |
|---|---|---|---|---|---|---|
[[[17, 20], [23, 23]]] | [["English", "is", "shown", "to", "be", "trans-context-free", "on", "the", "basis", "of", "coordinations", "of", "the", "respectively", "type", "that", "involve", "strictly", "syntactic", "cross-serial", "agreement", "."], ["The", "agreement", "in", "question", "involves", "number", "in", "nouns", "and", "reflexive", "pronouns", "and", "is", "syntactic", "rather", "than", "semantic", "in", "nature", "because", "grammatical", "number", "in", "English", ",", "like", "grammatical", "gender", "in", "languages", "such", "as", "French", ",", "is", "partly", "arbitrary", "."], ["The", "formal", "proof", ",", "which", "makes", "crucial", "use", "of", "the", "Interchange", "Lemma", "of", "Ogden", "et", "al.", ",", "is", "so", "constructed", "as", "to", "be", "valid", "even", "if", "English", "is", "presumed", "to", "contain", "grammatical", "sentences", "in", "which", "respectively", "operates", "across", "a", "pair", "of", "coordinate", "phrases", "one", "of", "whose", "members", "has", "fewer", "conjuncts", "than", "the", "other", ";", "it", "thus", "goes", "through", "whatever", "the", "facts", "may", "be", "regarding", "constructions", "with", "unequal", "numbers", "of", "conjuncts", "in", "the", "scope", "of", "respectively", ",", "whereas", "other", "arguments", "have", "foundered", "on", "this", "problem", "."]] | [[[0, 0, "Material"], [10, 10, "OtherScientificTerm"], [17, 20, "OtherScientificTerm"]], [[23, 23, "Generic"], [29, 29, "OtherScientificTerm"], [31, 32, "OtherScientificTerm"], [42, 43, "OtherScientificTerm"], [45, 45, "Material"], [48, 49, "OtherScientificTerm"], [51, 51, "Material"], [54, 54, "Material"]], [[70, 71, "Method"], [86, 86, "Material"]]] | [[], [[29, 29, 31, 32, "CONJUNCTION"], [48, 49, 51, 51, "FEATURE-OF"], [54, 54, 51, 51, "HYPONYM-OF"]], []] | J87-1003 | train | [{"sentence": "English is shown to be trans-context-free on the basis of coordinations of the respectively type that involve strictly syntactic cross-serial agreement .", "entities": [{"text": "English", "type": "Material"}, {"text": "coordinations", "type": "OtherScientificTerm"}, {"text": "strictly syntactic cross-serial agreement", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in languages such as French , is partly arbitrary .", "entities": [{"text": "agreement", "type": "Generic"}, {"text": "nouns", "type": "OtherScientificTerm"}, {"text": "reflexive pronouns", "type": "OtherScientificTerm"}, {"text": "grammatical number", "type": "OtherScientificTerm"}, {"text": "English", "type": "Material"}, {"text": "grammatical gender", "type": "OtherScientificTerm"}, {"text": "languages", "type": "Material"}, {"text": "French", "type": "Material"}], "relations": [{"head": "nouns", "tail": "reflexive pronouns", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "grammatical gender", "tail": "languages", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "Material"}, {"head": "French", "tail": "languages", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Material"}]}, {"sentence": "The formal proof , which makes crucial use of the Interchange Lemma of Ogden et al. , is so constructed as to be valid even if English is presumed to contain grammatical sentences in which respectively operates across a pair of coordinate phrases one of whose members has fewer conjuncts than the other ; it thus goes through whatever the facts may be regarding constructions with unequal numbers of conjuncts in the scope of respectively , whereas other arguments have foundered on this problem .", "entities": [{"text": "Interchange Lemma", "type": "Method"}, {"text": "English", "type": "Material"}], "relations": []}] |
[[[62, 64], [90, 91], [96, 98], [112, 114]], [[6, 6], [170, 170]], [[81, 82], [126, 127]], [[129, 131], [142, 142]]] | [["In", "this", "paper", ",", "a", "novel", "method", "to", "learn", "the", "intrinsic", "object", "structure", "for", "robust", "visual", "tracking", "is", "proposed", "."], ["The", "basic", "assumption", "is", "that", "the", "parameterized", "object", "state", "lies", "on", "a", "low", "dimensional", "manifold", "and", "can", "be", "learned", "from", "training", "data", "."], ["Based", "on", "this", "assumption", ",", "firstly", "we", "derived", "the", "dimensionality", "reduction", "and", "density", "estimation", "algorithm", "for", "unsupervised", "learning", "of", "object", "intrinsic", "representation", ",", "the", "obtained", "non-rigid", "part", "of", "object", "state", "reduces", "even", "to", "2", "dimensions", "."], ["Secondly", "the", "dynamical", "model", "is", "derived", "and", "trained", "based", "on", "this", "intrinsic", "representation", "."], ["Thirdly", "the", "learned", "intrinsic", "object", "structure", "is", "integrated", "into", "a", "particle-filter", "style", "tracker", "."], ["We", "will", "show", "that", "this", "intrinsic", "object", "representation", "has", "some", "interesting", "properties", "and", "based", "on", "which", "the", "newly", "derived", "dynamical", "model", "makes", "particle-filter", "style", "tracker", "more", "robust", "and", "reliable", "."], ["Experiments", "show", "that", "the", "learned", "tracker", "performs", "much", "better", "than", "existing", "trackers", "on", "the", "tracking", "of", "complex", "non-rigid", "motions", "such", "as", "fish", "twisting", "with", "self-occlusion", "and", "large", "inter-frame", "lip", "motion", "."], ["The", "proposed", "method", "also", "has", "the", "potential", "to", "solve", "other", "type", "of", "tracking", "problems", "."]] | [[[6, 6, "Method"], [10, 12, "OtherScientificTerm"], [14, 16, "Task"]], [[26, 28, "OtherScientificTerm"], [32, 34, "OtherScientificTerm"]], [[52, 57, "Method"], [59, 64, "Task"], [62, 64, "Method"], [68, 72, "OtherScientificTerm"]], [[81, 82, "Method"], [90, 91, "Method"]], [[96, 98, "OtherScientificTerm"], [103, 105, "Method"]], [[112, 114, "Method"], [126, 127, "Method"], [129, 131, "Method"]], [[142, 142, "Generic"], [148, 148, "Generic"], [151, 155, "Task"], [153, 155, "OtherScientificTerm"], [158, 159, "OtherScientificTerm"], [161, 161, "OtherScientificTerm"], [164, 166, "OtherScientificTerm"]], [[170, 170, "Generic"], [180, 181, "Task"]]] | [[[6, 6, 10, 12, "USED-FOR"], [10, 12, 14, 16, "USED-FOR"]], [[32, 34, 26, 28, "FEATURE-OF"]], [[52, 57, 59, 64, "USED-FOR"]], [[90, 91, 81, 82, "USED-FOR"]], [[96, 98, 103, 105, "PART-OF"]], [[126, 127, 129, 131, "USED-FOR"]], [[142, 142, 148, 148, "COMPARE"], [142, 142, 151, 155, "USED-FOR"], [148, 148, 151, 155, "USED-FOR"], [158, 159, 153, 155, "HYPONYM-OF"], [161, 161, 158, 159, "FEATURE-OF"], [161, 161, 164, 166, "CONJUNCTION"], [164, 166, 158, 159, "FEATURE-OF"]], [[170, 170, 180, 181, "USED-FOR"]]] | CVPR_2003_18_abs | train | [{"sentence": "In this paper , a novel method to learn the intrinsic object structure for robust visual tracking is proposed .", "entities": [{"text": "method", "type": "Method"}, {"text": "intrinsic object structure", "type": "OtherScientificTerm"}, {"text": "robust visual tracking", "type": "Task"}], "relations": [{"head": "method", "tail": "intrinsic object structure", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "intrinsic object structure", "tail": "robust visual tracking", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}]}, {"sentence": "The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data .", "entities": [{"text": "parameterized object state", "type": "OtherScientificTerm"}, {"text": "low dimensional manifold", "type": "OtherScientificTerm"}], "relations": [{"head": "low dimensional manifold", "tail": "parameterized object state", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Based on this assumption , firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation , the obtained non-rigid part of object state reduces even to 2 dimensions .", "entities": [{"text": "dimensionality reduction and density estimation algorithm", "type": "Method"}, {"text": "unsupervised learning of object intrinsic representation", "type": "Task"}, {"text": "object intrinsic representation", "type": "Method"}, {"text": "non-rigid part of object state", "type": "OtherScientificTerm"}], "relations": [{"head": "dimensionality reduction and density estimation algorithm", "tail": "unsupervised learning of object intrinsic representation", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}]}, {"sentence": "Secondly the dynamical model is derived and trained based on this intrinsic representation .", "entities": [{"text": "dynamical model", "type": "Method"}, {"text": "intrinsic representation", "type": "Method"}], "relations": [{"head": "intrinsic representation", "tail": "dynamical model", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker .", "entities": [{"text": "intrinsic object structure", "type": "OtherScientificTerm"}, {"text": "particle-filter style tracker", "type": "Method"}], "relations": [{"head": "intrinsic object structure", "tail": "particle-filter style tracker", "label": "PART-OF", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable .", "entities": [{"text": "intrinsic object representation", "type": "Method"}, {"text": "dynamical model", "type": "Method"}, {"text": "particle-filter style tracker", "type": "Method"}], "relations": [{"head": "dynamical model", "tail": "particle-filter style tracker", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion .", "entities": [{"text": "tracker", "type": "Generic"}, {"text": "trackers", "type": "Generic"}, {"text": "tracking of complex non-rigid motions", "type": "Task"}, {"text": "complex non-rigid motions", "type": "OtherScientificTerm"}, {"text": "fish twisting", "type": "OtherScientificTerm"}, {"text": "self-occlusion", "type": "OtherScientificTerm"}, {"text": "inter-frame lip motion", "type": "OtherScientificTerm"}], "relations": [{"head": "tracker", "tail": "trackers", "label": "COMPARE", "head_type": "Generic", "tail_type": "Generic"}, {"head": "tracker", "tail": "tracking of complex non-rigid motions", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "trackers", "tail": "tracking of complex non-rigid motions", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "fish twisting", "tail": "complex non-rigid motions", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "self-occlusion", "tail": "fish twisting", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "self-occlusion", "tail": "inter-frame lip motion", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "inter-frame lip motion", "tail": "fish twisting", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The proposed method also has the potential to solve other type of tracking problems .", "entities": [{"text": "method", "type": "Generic"}, {"text": "tracking problems", "type": "Task"}], "relations": [{"head": "method", "tail": "tracking problems", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}] |
[[[154, 154], [214, 214]], [[40, 44], [85, 85], [170, 170]], [[15, 21], [150, 151], [165, 166], [206, 207]], [[95, 95], [111, 111]]] | [["In", "this", "paper", ",", "we", "present", "a", "digital", "signal", "processor", "-LRB-", "DSP", "-RRB-", "implementation", "of", "real-time", "statistical", "voice", "conversion", "-LRB-", "VC", "-RRB-", "for", "silent", "speech", "enhancement", "and", "electrolaryngeal", "speech", "enhancement", "."], ["As", "a", "silent", "speech", "interface", ",", "we", "focus", "on", "non-audible", "murmur", "-LRB-", "NAM", "-RRB-", ",", "which", "can", "be", "used", "in", "situations", "where", "audible", "speech", "is", "not", "acceptable", "."], ["Electrolaryngeal", "speech", "is", "one", "of", "the", "typical", "types", "of", "alaryngeal", "speech", "produced", "by", "an", "alternative", "speaking", "method", "for", "laryngectomees", "."], ["However", ",", "the", "sound", "quality", "of", "NAM", "and", "electrolaryngeal", "speech", "suffers", "from", "lack", "of", "naturalness", "."], ["VC", "has", "proven", "to", "be", "one", "of", "the", "promising", "approaches", "to", "address", "this", "problem", ",", "and", "it", "has", "been", "successfully", "implemented", "on", "devices", "with", "sufficient", "computational", "resources", "."], ["An", "implementation", "on", "devices", "that", "are", "highly", "portable", "but", "have", "limited", "computational", "resources", "would", "greatly", "contribute", "to", "its", "practical", "use", "."], ["In", "this", "paper", "we", "further", "implement", "real-time", "VC", "on", "a", "DSP", "."], ["To", "implement", "the", "two", "speech", "enhancement", "systems", "based", "on", "real-time", "VC", ",", "one", "from", "NAM", "to", "a", "whispered", "voice", "and", "the", "other", "from", "electrolaryngeal", "speech", "to", "a", "natural", "voice", ",", "we", "propose", "several", "methods", "for", "reducing", "computational", "cost", "while", "preserving", "conversion", "accuracy", "."], ["We", "conduct", "experimental", "evaluations", "and", "show", "that", "real-time", "VC", "is", "capable", "of", "running", "on", "a", "DSP", "with", "little", "degradation", "."]] | [[[7, 13, "Method"], [15, 21, "Method"], [23, 25, "Task"], [27, 29, "Task"]], [[33, 35, "OtherScientificTerm"], [40, 44, "Material"], [53, 54, "Material"]], [[59, 60, "Material"], [68, 69, "Material"], [74, 75, "Method"], [77, 77, "OtherScientificTerm"]], [[82, 83, "Metric"], [85, 85, "Material"], [85, 88, "Material"]], [[95, 95, "Method"], [111, 111, "Generic"], [117, 117, "Generic"], [119, 121, "Material"]], [[126, 126, "Generic"], [133, 135, "Material"]], [[150, 151, "Method"], [154, 154, "OtherScientificTerm"]], [[160, 162, "Method"], [165, 166, "Method"], [168, 168, "Generic"], [170, 170, "Material"], [173, 174, "Material"], [177, 177, "Generic"], [179, 180, "Material"], [183, 184, "Material"], [189, 189, "Generic"], [192, 193, "Metric"], [196, 197, "Metric"]], [[206, 207, "Method"], [214, 214, "OtherScientificTerm"]]] | [[[7, 13, 15, 21, "USED-FOR"], [15, 21, 23, 25, "USED-FOR"], [15, 21, 27, 29, "USED-FOR"], [23, 25, 27, 29, "CONJUNCTION"]], [], [[59, 60, 68, 69, "HYPONYM-OF"], [74, 75, 68, 69, "USED-FOR"], [74, 75, 77, 77, "USED-FOR"]], [[82, 83, 85, 88, "EVALUATE-FOR"]], [[117, 117, 111, 111, "USED-FOR"], [119, 121, 117, 117, "FEATURE-OF"]], [[133, 135, 126, 126, "FEATURE-OF"]], [[154, 154, 150, 151, "USED-FOR"]], [[165, 166, 160, 162, "USED-FOR"], [168, 168, 160, 162, "HYPONYM-OF"], [168, 168, 177, 177, "CONJUNCTION"], [177, 177, 160, 162, "HYPONYM-OF"], [192, 193, 189, 189, "EVALUATE-FOR"], [192, 193, 196, 197, "CONJUNCTION"], [196, 197, 189, 189, "EVALUATE-FOR"]], [[214, 214, 206, 207, "USED-FOR"]]] | INTERSPEECH_2013_31_abs | train | [{"sentence": "In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of real-time statistical voice conversion -LRB- VC -RRB- for silent speech enhancement and electrolaryngeal speech enhancement .", "entities": [{"text": "digital signal processor -LRB- DSP -RRB- implementation", "type": "Method"}, {"text": "real-time statistical voice conversion -LRB- VC -RRB-", "type": "Method"}, {"text": "silent speech enhancement", "type": "Task"}, {"text": "electrolaryngeal speech enhancement", "type": "Task"}], "relations": [{"head": "digital signal processor -LRB- DSP -RRB- implementation", "tail": "real-time statistical voice conversion -LRB- VC -RRB-", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}, {"head": "real-time statistical voice conversion -LRB- VC -RRB-", "tail": "silent speech enhancement", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "real-time statistical voice conversion -LRB- VC -RRB-", "tail": "electrolaryngeal speech enhancement", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "silent speech enhancement", "tail": "electrolaryngeal speech enhancement", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "As a silent speech interface , we focus on non-audible murmur -LRB- NAM -RRB- , which can be used in situations where audible speech is not acceptable .", "entities": [{"text": "silent speech interface", "type": "OtherScientificTerm"}, {"text": "non-audible murmur -LRB- NAM -RRB-", "type": "Material"}, {"text": "audible speech", "type": "Material"}], "relations": []}, {"sentence": "Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative speaking method for laryngectomees .", "entities": [{"text": "Electrolaryngeal speech", "type": "Material"}, {"text": "alaryngeal speech", "type": "Material"}, {"text": "speaking method", "type": "Method"}, {"text": "laryngectomees", "type": "OtherScientificTerm"}], "relations": [{"head": "Electrolaryngeal speech", "tail": "alaryngeal speech", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Material"}, {"head": "speaking method", "tail": "alaryngeal speech", "label": "USED-FOR", "head_type": "Method", "tail_type": "Material"}, {"head": "speaking method", "tail": "laryngectomees", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "However , the sound quality of NAM and electrolaryngeal speech suffers from lack of naturalness .", "entities": [{"text": "sound quality", "type": "Metric"}, {"text": "NAM", "type": "Material"}, {"text": "NAM and electrolaryngeal speech", "type": "Material"}], "relations": [{"head": "sound quality", "tail": "NAM and electrolaryngeal speech", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Material"}]}, {"sentence": "VC has proven to be one of the promising approaches to address this problem , and it has been successfully implemented on devices with sufficient computational resources .", "entities": [{"text": "VC", "type": "Method"}, {"text": "it", "type": "Generic"}, {"text": "devices", "type": "Generic"}, {"text": "sufficient computational resources", "type": "Material"}], "relations": [{"head": "devices", "tail": "it", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Generic"}, {"head": "sufficient computational resources", "tail": "devices", "label": "FEATURE-OF", "head_type": "Material", "tail_type": "Generic"}]}, {"sentence": "An implementation on devices that are highly portable but have limited computational resources would greatly contribute to its practical use .", "entities": [{"text": "devices", "type": "Generic"}, {"text": "limited computational resources", "type": "Material"}], "relations": [{"head": "limited computational resources", "tail": "devices", "label": "FEATURE-OF", "head_type": "Material", "tail_type": "Generic"}]}, {"sentence": "In this paper we further implement real-time VC on a DSP .", "entities": [{"text": "real-time VC", "type": "Method"}, {"text": "DSP", "type": "OtherScientificTerm"}], "relations": [{"head": "DSP", "tail": "real-time VC", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .", "entities": [{"text": "speech enhancement systems", "type": "Method"}, {"text": "real-time VC", "type": "Method"}, {"text": "one", "type": "Generic"}, {"text": "NAM", "type": "Material"}, {"text": "whispered voice", "type": "Material"}, {"text": "other", "type": "Generic"}, {"text": "electrolaryngeal speech", "type": "Material"}, {"text": "natural voice", "type": "Material"}, {"text": "methods", "type": "Generic"}, {"text": "computational cost", "type": "Metric"}, {"text": "conversion accuracy", "type": "Metric"}], "relations": [{"head": "real-time VC", "tail": "speech enhancement systems", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}, {"head": "one", "tail": "speech enhancement systems", "label": "HYPONYM-OF", "head_type": "Generic", "tail_type": "Method"}, {"head": "one", "tail": "other", "label": "CONJUNCTION", "head_type": "Generic", "tail_type": "Generic"}, {"head": "other", "tail": "speech enhancement systems", "label": "HYPONYM-OF", "head_type": "Generic", "tail_type": "Method"}, {"head": "computational cost", "tail": "methods", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Generic"}, {"head": "computational cost", "tail": "conversion accuracy", "label": "CONJUNCTION", "head_type": "Metric", "tail_type": "Metric"}, {"head": "conversion accuracy", "tail": "methods", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Generic"}]}, {"sentence": "We conduct experimental evaluations and show that real-time VC is capable of running on a DSP with little degradation .", "entities": [{"text": "real-time VC", "type": "Method"}, {"text": "DSP", "type": "OtherScientificTerm"}], "relations": [{"head": "DSP", "tail": "real-time VC", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}] |
[[[3, 3], [110, 110]]] | [["We", "propose", "a", "method", "that", "automatically", "generates", "paraphrase", "sets", "from", "seed", "sentences", "to", "be", "used", "as", "reference", "sets", "in", "objective", "machine", "translation", "evaluation", "measures", "like", "BLEU", "and", "NIST", "."], ["We", "measured", "the", "quality", "of", "the", "paraphrases", "produced", "in", "an", "experiment", ",", "i.e.", ",", "-LRB-", "i", "-RRB-", "their", "grammaticality", ":", "at", "least", "99", "%", "correct", "sentences", ";", "-LRB-", "ii", "-RRB-", "their", "equivalence", "in", "meaning", ":", "at", "least", "96", "%", "correct", "paraphrases", "either", "by", "meaning", "equivalence", "or", "entailment", ";", "and", ",", "-LRB-", "iii", "-RRB-", "the", "amount", "of", "internal", "lexical", "and", "syntactical", "variation", "in", "a", "set", "of", "paraphrases", ":", "slightly", "superior", "to", "that", "of", "hand-produced", "sets", "."], ["The", "paraphrase", "sets", "produced", "by", "this", "method", "thus", "seem", "adequate", "as", "reference", "sets", "to", "be", "used", "for", "MT", "evaluation", "."]] | [[[3, 3, "Generic"], [7, 7, "OtherScientificTerm"], [20, 23, "Metric"], [25, 25, "Metric"], [27, 27, "Metric"]], [[35, 35, "OtherScientificTerm"], [47, 47, "Metric"], [60, 62, "Metric"], [69, 69, "OtherScientificTerm"], [72, 73, "Method"], [75, 75, "Method"], [85, 89, "Metric"], [94, 94, "OtherScientificTerm"], [101, 102, "OtherScientificTerm"]], [[105, 105, "OtherScientificTerm"], [110, 110, "Generic"], [121, 122, "Task"]]] | [[[3, 3, 7, 7, "USED-FOR"], [7, 7, 20, 23, "USED-FOR"], [25, 25, 20, 23, "HYPONYM-OF"], [25, 25, 27, 27, "CONJUNCTION"], [27, 27, 20, 23, "HYPONYM-OF"]], [[60, 62, 47, 47, "CONJUNCTION"], [72, 73, 69, 69, "USED-FOR"], [72, 73, 75, 75, "CONJUNCTION"], [75, 75, 69, 69, "USED-FOR"], [85, 89, 60, 62, "CONJUNCTION"], [94, 94, 101, 102, "COMPARE"]], [[110, 110, 105, 105, "USED-FOR"]]] | I05-5008 | train | [{"sentence": "We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST .", "entities": [{"text": "method", "type": "Generic"}, {"text": "paraphrase", "type": "OtherScientificTerm"}, {"text": "machine translation evaluation measures", "type": "Metric"}, {"text": "BLEU", "type": "Metric"}, {"text": "NIST", "type": "Metric"}], "relations": [{"head": "method", "tail": "paraphrase", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "paraphrase", "tail": "machine translation evaluation measures", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Metric"}, {"head": "BLEU", "tail": "machine translation evaluation measures", "label": "HYPONYM-OF", "head_type": "Metric", "tail_type": "Metric"}, {"head": "BLEU", "tail": "NIST", "label": "CONJUNCTION", "head_type": "Metric", "tail_type": "Metric"}, {"head": "NIST", "tail": "machine translation evaluation measures", "label": "HYPONYM-OF", "head_type": "Metric", "tail_type": "Metric"}]}, {"sentence": "We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .", "entities": [{"text": "paraphrases", "type": "OtherScientificTerm"}, {"text": "grammaticality", "type": "Metric"}, {"text": "equivalence in meaning", "type": "Metric"}, {"text": "paraphrases", "type": "OtherScientificTerm"}, {"text": "meaning equivalence", "type": "Method"}, {"text": "entailment", "type": "Method"}, {"text": "internal lexical and syntactical variation", "type": "Metric"}, {"text": "paraphrases", "type": "OtherScientificTerm"}, {"text": "hand-produced sets", "type": "OtherScientificTerm"}], "relations": [{"head": "equivalence in meaning", "tail": "grammaticality", "label": "CONJUNCTION", "head_type": "Metric", "tail_type": "Metric"}, {"head": "meaning equivalence", "tail": "paraphrases", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "meaning equivalence", "tail": "entailment", "label": "CONJUNCTION", "head_type": "Method", "tail_type": "Method"}, {"head": "entailment", "tail": "paraphrases", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "internal lexical and syntactical variation", "tail": "equivalence in meaning", "label": "CONJUNCTION", "head_type": "Metric", "tail_type": "Metric"}, {"head": "paraphrases", "tail": "hand-produced sets", "label": "COMPARE", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The paraphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation .", "entities": [{"text": "paraphrase", "type": "OtherScientificTerm"}, {"text": "method", "type": "Generic"}, {"text": "MT evaluation", "type": "Task"}], "relations": [{"head": "method", "tail": "paraphrase", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}] |
[[[35, 35], [69, 69]]] | [["Graph", "unification", "remains", "the", "most", "expensive", "part", "of", "unification-based", "grammar", "parsing", "."], ["We", "focus", "on", "one", "speed-up", "element", "in", "the", "design", "of", "unification", "algorithms", ":", "avoidance", "of", "copying", "of", "unmodified", "subgraphs", "."], ["We", "propose", "a", "method", "of", "attaining", "such", "a", "design", "through", "a", "method", "of", "structure-sharing", "which", "avoids", "log", "-LRB-", "d", "-RRB-", "overheads", "often", "associated", "with", "structure-sharing", "of", "graphs", "without", "any", "use", "of", "costly", "dependency", "pointers", "."], ["The", "proposed", "scheme", "eliminates", "redundant", "copying", "while", "maintaining", "the", "quasi-destructive", "scheme", "'s", "ability", "to", "avoid", "over", "copying", "and", "early", "copying", "combined", "with", "its", "ability", "to", "handle", "cyclic", "structures", "without", "algorithmic", "additions", "."]] | [[[0, 1, "Task"], [8, 10, "Task"]], [[16, 17, "Method"], [22, 23, "Method"], [27, 30, "OtherScientificTerm"], [29, 30, "OtherScientificTerm"]], [[35, 35, "Generic"], [45, 45, "Method"], [48, 52, "OtherScientificTerm"], [56, 58, "OtherScientificTerm"], [64, 65, "Method"]], [[69, 69, "Generic"], [71, 72, "OtherScientificTerm"], [76, 79, "OtherScientificTerm"], [82, 83, "OtherScientificTerm"], [85, 86, "OtherScientificTerm"], [93, 94, "OtherScientificTerm"]]] | [[[0, 1, 8, 10, "PART-OF"]], [[16, 17, 22, 23, "PART-OF"]], [[45, 45, 35, 35, "USED-FOR"]], [[69, 69, 93, 94, "USED-FOR"], [76, 79, 69, 69, "FEATURE-OF"], [82, 83, 85, 86, "CONJUNCTION"]]] | C92-2068 | train | [{"sentence": "Graph unification remains the most expensive part of unification-based grammar parsing .", "entities": [{"text": "Graph unification", "type": "Task"}, {"text": "unification-based grammar parsing", "type": "Task"}], "relations": [{"head": "Graph unification", "tail": "unification-based grammar parsing", "label": "PART-OF", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "We focus on one speed-up element in the design of unification algorithms : avoidance of copying of unmodified subgraphs .", "entities": [{"text": "speed-up element", "type": "Method"}, {"text": "unification algorithms", "type": "Method"}, {"text": "copying of unmodified subgraphs", "type": "OtherScientificTerm"}, {"text": "unmodified subgraphs", "type": "OtherScientificTerm"}], "relations": [{"head": "speed-up element", "tail": "unification algorithms", "label": "PART-OF", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "We propose a method of attaining such a design through a method of structure-sharing which avoids log -LRB- d -RRB- overheads often associated with structure-sharing of graphs without any use of costly dependency pointers .", "entities": [{"text": "method", "type": "Generic"}, {"text": "structure-sharing", "type": "Method"}, {"text": "log -LRB- d -RRB- overheads", "type": "OtherScientificTerm"}, {"text": "structure-sharing of graphs", "type": "OtherScientificTerm"}, {"text": "dependency pointers", "type": "Method"}], "relations": [{"head": "structure-sharing", "tail": "method", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}]}, {"sentence": "The proposed scheme eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions .", "entities": [{"text": "scheme", "type": "Generic"}, {"text": "redundant copying", "type": "OtherScientificTerm"}, {"text": "quasi-destructive scheme 's ability", "type": "OtherScientificTerm"}, {"text": "over copying", "type": "OtherScientificTerm"}, {"text": "early copying", "type": "OtherScientificTerm"}, {"text": "cyclic structures", "type": "OtherScientificTerm"}], "relations": [{"head": "scheme", "tail": "cyclic structures", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "quasi-destructive scheme 's ability", "tail": "scheme", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "over copying", "tail": "early copying", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}] |
[[[7, 7], [86, 86]]] | [["We", "describe", "a", "novel", "technique", "and", "implemented", "system", "for", "constructing", "a", "subcategorization", "dictionary", "from", "textual", "corpora", "."], ["Each", "dictionary", "entry", "encodes", "the", "relative", "frequency", "of", "occurrence", "of", "a", "comprehensive", "set", "of", "subcategorization", "classes", "for", "English", "."], ["An", "initial", "experiment", ",", "on", "a", "sample", "of", "14", "verbs", "which", "exhibit", "multiple", "complementation", "patterns", ",", "demonstrates", "that", "the", "technique", "achieves", "accuracy", "comparable", "to", "previous", "approaches", ",", "which", "are", "all", "limited", "to", "a", "highly", "restricted", "set", "of", "subcategorization", "classes", "."], ["We", "also", "demonstrate", "that", "a", "subcategorization", "dictionary", "built", "with", "the", "system", "improves", "the", "accuracy", "of", "a", "parser", "by", "an", "appreciable", "amount"]] | [[[7, 7, "Generic"], [11, 12, "OtherScientificTerm"], [14, 15, "Material"]], [[22, 25, "OtherScientificTerm"]], [], [[81, 82, "OtherScientificTerm"], [86, 86, "Generic"], [89, 89, "Metric"], [92, 92, "Method"]]] | [[[7, 7, 11, 12, "USED-FOR"], [14, 15, 7, 7, "USED-FOR"]], [], [], [[86, 86, 81, 82, "USED-FOR"], [89, 89, 92, 92, "EVALUATE-FOR"], [92, 92, 81, 82, "EVALUATE-FOR"]]] | A97-1052 | train | [{"sentence": "We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora .", "entities": [{"text": "system", "type": "Generic"}, {"text": "subcategorization dictionary", "type": "OtherScientificTerm"}, {"text": "textual corpora", "type": "Material"}], "relations": [{"head": "system", "tail": "subcategorization dictionary", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "textual corpora", "tail": "system", "label": "USED-FOR", "head_type": "Material", "tail_type": "Generic"}]}, {"sentence": "Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English .", "entities": [{"text": "relative frequency of occurrence", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "An initial experiment , on a sample of 14 verbs which exhibit multiple complementation patterns , demonstrates that the technique achieves accuracy comparable to previous approaches , which are all limited to a highly restricted set of subcategorization classes .", "entities": [], "relations": []}, {"sentence": "We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount", "entities": [{"text": "subcategorization dictionary", "type": "OtherScientificTerm"}, {"text": "system", "type": "Generic"}, {"text": "accuracy", "type": "Metric"}, {"text": "parser", "type": "Method"}], "relations": [{"head": "system", "tail": "subcategorization dictionary", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "accuracy", "tail": "parser", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Method"}, {"head": "parser", "tail": "subcategorization dictionary", "label": "EVALUATE-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}] |
[[[122, 124], [175, 176], [185, 185]], [[26, 28], [76, 76]], [[49, 51], [55, 55], [65, 65]], [[112, 115], [127, 127]]] | [["The", "introduction", "of", "prior", "knowledge", "has", "greatly", "enhanced", "numerous", "purely", "low-level", "driven", "image", "processing", "algorithms", "."], ["In", "this", "work", ",", "we", "focus", "on", "the", "problem", "of", "non-rigid", "image", "registration", "."], ["A", "number", "of", "powerful", "registration", "criteria", "have", "been", "developed", "in", "the", "last", "decade", ",", "most", "prominently", "the", "criterion", "of", "maximum", "mutual", "information", "."], ["Although", "this", "criterion", "provides", "for", "good", "registration", "results", "in", "many", "applications", ",", "it", "remains", "a", "purely", "low-level", "criterion", "."], ["As", "a", "consequence", ",", "registration", "results", "will", "deteriorate", "once", "this", "low-level", "information", "is", "corrupted", ",", "due", "to", "noise", ",", "partial", "occlusions", "or", "missing", "image", "structure", "."], ["In", "this", "paper", ",", "we", "will", "develop", "a", "Bayesian", "framework", "that", "allows", "to", "impose", "statistically", "learned", "prior", "knowledge", "about", "the", "joint", "intensity", "distribution", "into", "image", "registration", "methods", "."], ["The", "prior", "is", "given", "by", "a", "kernel", "density", "estimate", "on", "the", "space", "of", "joint", "intensity", "distributions", "computed", "from", "a", "representative", "set", "of", "pre-registered", "image", "pairs", "."], ["This", "nonparametric", "prior", "accurately", "models", "previously", "learned", "intensity", "relations", "between", "various", "image", "modalities", "and", "slice", "locations", "."], ["Experimental", "results", "demonstrate", "that", "the", "resulting", "registration", "process", "is", "more", "robust", "to", "missing", "low-level", "information", "as", "it", "favors", "intensity", "correspondences", "statistically", "consistent", "with", "the", "learned", "intensity", "distributions", "."]] | [[[3, 4, "OtherScientificTerm"], [10, 14, "Method"]], [[26, 28, "Task"]], [[34, 35, "Metric"], [49, 51, "Metric"]], [[55, 55, "Generic"], [65, 65, "Generic"], [69, 70, "Metric"]], [[76, 76, "Task"], [82, 83, "OtherScientificTerm"], [89, 89, "OtherScientificTerm"], [91, 92, "OtherScientificTerm"], [94, 96, "OtherScientificTerm"]], [[106, 107, "Method"], [112, 115, "OtherScientificTerm"], [118, 120, "OtherScientificTerm"], [122, 124, "Method"]], [[127, 127, "Generic"], [132, 134, "Method"], [139, 141, "OtherScientificTerm"], [148, 150, "Material"]], [[159, 160, "OtherScientificTerm"], [163, 164, "OtherScientificTerm"], [166, 167, "OtherScientificTerm"]], [[175, 176, "Method"], [181, 183, "OtherScientificTerm"], [185, 185, "OtherScientificTerm"], [187, 188, "OtherScientificTerm"], [194, 195, "OtherScientificTerm"]]] | [[], [], [[49, 51, 34, 35, "HYPONYM-OF"]], [[69, 70, 65, 65, "FEATURE-OF"]], [], [[106, 107, 122, 124, "USED-FOR"], [112, 115, 122, 124, "USED-FOR"], [118, 120, 112, 115, "FEATURE-OF"]], [[132, 134, 127, 127, "USED-FOR"], [132, 134, 139, 141, "USED-FOR"], [148, 150, 139, 141, "USED-FOR"]], [], [[175, 176, 181, 183, "USED-FOR"], [185, 185, 187, 188, "USED-FOR"]]] | CVPR_2006_21_abs | train | [{"sentence": "The introduction of prior knowledge has greatly enhanced numerous purely low-level driven image processing algorithms .", "entities": [{"text": "prior knowledge", "type": "OtherScientificTerm"}, {"text": "low-level driven image processing algorithms", "type": "Method"}], "relations": []}, {"sentence": "In this work , we focus on the problem of non-rigid image registration .", "entities": [{"text": "non-rigid image registration", "type": "Task"}], "relations": []}, {"sentence": "A number of powerful registration criteria have been developed in the last decade , most prominently the criterion of maximum mutual information .", "entities": [{"text": "registration criteria", "type": "Metric"}, {"text": "maximum mutual information", "type": "Metric"}], "relations": [{"head": "maximum mutual information", "tail": "registration criteria", "label": "HYPONYM-OF", "head_type": "Metric", "tail_type": "Metric"}]}, {"sentence": "Although this criterion provides for good registration results in many applications , it remains a purely low-level criterion .", "entities": [{"text": "criterion", "type": "Generic"}, {"text": "it", "type": "Generic"}, {"text": "low-level criterion", "type": "Metric"}], "relations": [{"head": "low-level criterion", "tail": "it", "label": "FEATURE-OF", "head_type": "Metric", "tail_type": "Generic"}]}, {"sentence": "As a consequence , registration results will deteriorate once this low-level information is corrupted , due to noise , partial occlusions or missing image structure .", "entities": [{"text": "registration", "type": "Task"}, {"text": "low-level information", "type": "OtherScientificTerm"}, {"text": "noise", "type": "OtherScientificTerm"}, {"text": "partial occlusions", "type": "OtherScientificTerm"}, {"text": "missing image structure", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "In this paper , we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods .", "entities": [{"text": "Bayesian framework", "type": "Method"}, {"text": "statistically learned prior knowledge", "type": "OtherScientificTerm"}, {"text": "joint intensity distribution", "type": "OtherScientificTerm"}, {"text": "image registration methods", "type": "Method"}], "relations": [{"head": "Bayesian framework", "tail": "image registration methods", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}, {"head": "statistically learned prior knowledge", "tail": "image registration methods", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "joint intensity distribution", "tail": "statistically learned prior knowledge", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs .", "entities": [{"text": "prior", "type": "Generic"}, {"text": "kernel density estimate", "type": "Method"}, {"text": "joint intensity distributions", "type": "OtherScientificTerm"}, {"text": "pre-registered image pairs", "type": "Material"}], "relations": [{"head": "kernel density estimate", "tail": "prior", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}, {"head": "kernel density estimate", "tail": "joint intensity distributions", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "pre-registered image pairs", "tail": "joint intensity distributions", "label": "USED-FOR", "head_type": "Material", "tail_type": "OtherScientificTerm"}]}, {"sentence": "This nonparametric prior accurately models previously learned intensity relations between various image modalities and slice locations .", "entities": [{"text": "intensity relations", "type": "OtherScientificTerm"}, {"text": "image modalities", "type": "OtherScientificTerm"}, {"text": "slice locations", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions .", "entities": [{"text": "registration process", "type": "Method"}, {"text": "missing low-level information", "type": "OtherScientificTerm"}, {"text": "it", "type": "OtherScientificTerm"}, {"text": "intensity correspondences", "type": "OtherScientificTerm"}, {"text": "intensity distributions", "type": "OtherScientificTerm"}], "relations": [{"head": "registration process", "tail": "missing low-level information", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "it", "tail": "intensity correspondences", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}] |
[[[73, 74], [125, 125]]] | [["There", "is", "an", "increased", "interest", "in", "the", "efficient", "creation", "of", "city", "models", ",", "be", "it", "virtual", "or", "as-built", "."], ["We", "present", "a", "method", "for", "synthesizing", "complex", ",", "photo-realistic", "facade", "images", ",", "from", "a", "single", "example", "."], ["After", "parsing", "the", "example", "image", "into", "its", "semantic", "components", ",", "a", "tiling", "for", "it", "is", "generated", "."], ["Novel", "tilings", "can", "then", "be", "created", ",", "yielding", "facade", "textures", "with", "different", "dimensions", "or", "with", "occluded", "parts", "inpainted", "."], ["A", "genetic", "algorithm", "guides", "the", "novel", "facades", "as", "well", "as", "inpainted", "parts", "to", "be", "consistent", "with", "the", "example", ",", "both", "in", "terms", "of", "their", "overall", "structure", "and", "their", "detailed", "textures", "."], ["Promising", "results", "for", "multiple", "standard", "datasets", "--", "in", "particular", "for", "the", "different", "building", "styles", "they", "contain", "--", "demonstrate", "the", "potential", "of", "the", "method", "."]] | [[[8, 11, "Task"]], [[22, 22, "Generic"], [24, 29, "Task"]], [[43, 44, "Method"], [47, 47, "OtherScientificTerm"]], [[54, 54, "OtherScientificTerm"], [61, 62, "OtherScientificTerm"], [68, 70, "OtherScientificTerm"]], [[73, 74, "Generic"], [78, 78, "OtherScientificTerm"], [82, 83, "OtherScientificTerm"]], [[106, 108, "Generic"], [125, 125, "Generic"]]] | [[], [[22, 22, 24, 29, "USED-FOR"]], [[47, 47, 43, 44, "USED-FOR"]], [[68, 70, 61, 62, "FEATURE-OF"]], [[73, 74, 78, 78, "USED-FOR"], [73, 74, 82, 83, "USED-FOR"]], [[106, 108, 125, 125, "EVALUATE-FOR"]]] | ICCV_2013_36_abs | train | [{"sentence": "There is an increased interest in the efficient creation of city models , be it virtual or as-built .", "entities": [{"text": "creation of city models", "type": "Task"}], "relations": []}, {"sentence": "We present a method for synthesizing complex , photo-realistic facade images , from a single example .", "entities": [{"text": "method", "type": "Generic"}, {"text": "synthesizing complex , photo-realistic facade images", "type": "Task"}], "relations": [{"head": "method", "tail": "synthesizing complex , photo-realistic facade images", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}, {"sentence": "After parsing the example image into its semantic components , a tiling for it is generated .", "entities": [{"text": "semantic components", "type": "Method"}, {"text": "tiling", "type": "OtherScientificTerm"}], "relations": [{"head": "tiling", "tail": "semantic components", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "Novel tilings can then be created , yielding facade textures with different dimensions or with occluded parts inpainted .", "entities": [{"text": "tilings", "type": "OtherScientificTerm"}, {"text": "facade textures", "type": "OtherScientificTerm"}, {"text": "occluded parts inpainted", "type": "OtherScientificTerm"}], "relations": [{"head": "occluded parts inpainted", "tail": "facade textures", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "A genetic algorithm guides the novel facades as well as inpainted parts to be consistent with the example , both in terms of their overall structure and their detailed textures .", "entities": [{"text": "genetic algorithm", "type": "Generic"}, {"text": "facades", "type": "OtherScientificTerm"}, {"text": "inpainted parts", "type": "OtherScientificTerm"}], "relations": [{"head": "genetic algorithm", "tail": "facades", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "genetic algorithm", "tail": "inpainted parts", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Promising results for multiple standard datasets -- in particular for the different building styles they contain -- demonstrate the potential of the method .", "entities": [{"text": "multiple standard datasets", "type": "Generic"}, {"text": "method", "type": "Generic"}], "relations": [{"head": "multiple standard datasets", "tail": "method", "label": "EVALUATE-FOR", "head_type": "Generic", "tail_type": "Generic"}]}] |
[[[9, 9], [11, 11], [37, 37], [81, 82], [84, 84]]] | [["We", "introduce", "a", "new", "interactive", "corpus", "exploration", "tool", "called", "InfoMagnets", "."], ["InfoMagnets", "aims", "at", "making", "exploratory", "corpus", "analysis", "accessible", "to", "researchers", "who", "are", "not", "experts", "in", "text", "mining", "."], ["As", "evidence", "of", "its", "usefulness", "and", "usability", ",", "it", "has", "been", "used", "successfully", "in", "a", "research", "context", "to", "uncover", "relationships", "between", "language", "and", "behavioral", "patterns", "in", "two", "distinct", "domains", ":", "tutorial", "dialogue", "-LRB-", "Kumar", "et", "al.", ",", "submitted", "-RRB-", "and", "on-line", "communities", "-LRB-", "Arguello", "et", "al.", ",", "2006", "-RRB-", "."], ["As", "an", "educational", "tool", ",", "it", "has", "been", "used", "as", "part", "of", "a", "unit", "on", "protocol", "analysis", "in", "an", "Educational", "Research", "Methods", "course", "."]] | [[[4, 7, "Method"], [9, 9, "Method"]], [[11, 11, "Method"], [15, 17, "Task"], [26, 27, "Task"]], [[37, 37, "Generic"], [57, 57, "Generic"], [59, 60, "Material"], [69, 70, "Material"]], [[81, 82, "Method"], [84, 84, "Generic"], [94, 95, "Task"]]] | [[[9, 9, 4, 7, "HYPONYM-OF"]], [[11, 11, 15, 17, "USED-FOR"]], [[37, 37, 57, 57, "USED-FOR"], [59, 60, 57, 57, "HYPONYM-OF"], [59, 60, 69, 70, "CONJUNCTION"], [69, 70, 57, 57, "HYPONYM-OF"]], [[81, 82, 94, 95, "USED-FOR"]]] | N06-4001 | train | [{"sentence": "We introduce a new interactive corpus exploration tool called InfoMagnets .", "entities": [{"text": "interactive corpus exploration tool", "type": "Method"}, {"text": "InfoMagnets", "type": "Method"}], "relations": [{"head": "InfoMagnets", "tail": "interactive corpus exploration tool", "label": "HYPONYM-OF", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining .", "entities": [{"text": "InfoMagnets", "type": "Method"}, {"text": "exploratory corpus analysis", "type": "Task"}, {"text": "text mining", "type": "Task"}], "relations": [{"head": "InfoMagnets", "tail": "exploratory corpus analysis", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}]}, {"sentence": "As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- .", "entities": [{"text": "it", "type": "Generic"}, {"text": "domains", "type": "Generic"}, {"text": "tutorial dialogue", "type": "Material"}, {"text": "on-line communities", "type": "Material"}], "relations": [{"head": "it", "tail": "domains", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Generic"}, {"head": "tutorial dialogue", "tail": "domains", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Generic"}, {"head": "tutorial dialogue", "tail": "on-line communities", "label": "CONJUNCTION", "head_type": "Material", "tail_type": "Material"}, {"head": "on-line communities", "tail": "domains", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Generic"}]}, {"sentence": "As an educational tool , it has been used as part of a unit on protocol analysis in an Educational Research Methods course .", "entities": [{"text": "educational tool", "type": "Method"}, {"text": "it", "type": "Generic"}, {"text": "protocol analysis", "type": "Task"}], "relations": [{"head": "educational tool", "tail": "protocol analysis", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}]}] |
[] | [["Sources", "of", "training", "data", "suitable", "for", "language", "modeling", "of", "conversational", "speech", "are", "limited", "."], ["In", "this", "paper", ",", "we", "show", "how", "training", "data", "can", "be", "supplemented", "with", "text", "from", "the", "web", "filtered", "to", "match", "the", "style", "and/or", "topic", "of", "the", "target", "recognition", "task", ",", "but", "also", "that", "it", "is", "possible", "to", "get", "bigger", "performance", "gains", "from", "the", "data", "by", "using", "class-dependent", "interpolation", "of", "N-grams", "."]] | [[[6, 7, "Method"], [9, 10, "Material"]], [[41, 42, "Task"], [60, 63, "Method"]]] | [[[9, 10, 6, 7, "USED-FOR"]], [[60, 63, 41, 42, "USED-FOR"]]] | N03-2003 | train | [{"sentence": "Sources of training data suitable for language modeling of conversational speech are limited .", "entities": [{"text": "language modeling", "type": "Method"}, {"text": "conversational speech", "type": "Material"}], "relations": [{"head": "conversational speech", "tail": "language modeling", "label": "USED-FOR", "head_type": "Material", "tail_type": "Method"}]}, {"sentence": "In this paper , we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task , but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams .", "entities": [{"text": "recognition task", "type": "Task"}, {"text": "class-dependent interpolation of N-grams", "type": "Method"}], "relations": [{"head": "class-dependent interpolation of N-grams", "tail": "recognition task", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}]}] |
[[[18, 18], [36, 36], [60, 60], [86, 86]], [[3, 3], [12, 12]]] | [["We", "present", "a", "method", "for", "detecting", "3D", "objects", "using", "multi-modalities", "."], ["While", "it", "is", "generic", ",", "we", "demonstrate", "it", "on", "the", "combination", "of", "an", "image", "and", "a", "dense", "depth", "map", "which", "give", "complementary", "object", "information", "."], ["It", "works", "in", "real-time", ",", "under", "heavy", "clutter", ",", "does", "not", "require", "a", "time", "consuming", "training", "stage", ",", "and", "can", "handle", "untextured", "objects", "."], ["It", "is", "based", "on", "an", "efficient", "representation", "of", "templates", "that", "capture", "the", "different", "modalities", ",", "and", "we", "show", "in", "many", "experiments", "on", "commodity", "hardware", "that", "our", "approach", "significantly", "outperforms", "state-of-the-art", "methods", "on", "single", "modalities", "."]] | [[[3, 3, "Generic"], [5, 7, "Task"], [9, 9, "OtherScientificTerm"]], [[12, 12, "Generic"], [18, 18, "Generic"], [24, 24, "OtherScientificTerm"], [27, 29, "OtherScientificTerm"], [32, 34, "OtherScientificTerm"]], [[36, 36, "Generic"], [49, 52, "OtherScientificTerm"], [57, 58, "OtherScientificTerm"]], [[60, 60, "Generic"], [68, 68, "OtherScientificTerm"], [73, 73, "OtherScientificTerm"], [82, 83, "OtherScientificTerm"], [86, 86, "Generic"], [89, 90, "Generic"], [92, 93, "OtherScientificTerm"]]] | [[[3, 3, 5, 7, "USED-FOR"], [9, 9, 3, 3, "USED-FOR"]], [[12, 12, 18, 18, "USED-FOR"], [24, 24, 18, 18, "USED-FOR"], [24, 24, 27, 29, "CONJUNCTION"], [27, 29, 18, 18, "USED-FOR"], [32, 34, 27, 29, "FEATURE-OF"]], [], [[68, 68, 73, 73, "USED-FOR"], [86, 86, 89, 90, "COMPARE"], [86, 86, 92, 93, "USED-FOR"], [89, 90, 92, 93, "USED-FOR"]]] | ICCV_2011_47_abs | train | [{"sentence": "We present a method for detecting 3D objects using multi-modalities .", "entities": [{"text": "method", "type": "Generic"}, {"text": "detecting 3D objects", "type": "Task"}, {"text": "multi-modalities", "type": "OtherScientificTerm"}], "relations": [{"head": "method", "tail": "detecting 3D objects", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "multi-modalities", "tail": "method", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}]}, {"sentence": "While it is generic , we demonstrate it on the combination of an image and a dense depth map which give complementary object information .", "entities": [{"text": "it", "type": "Generic"}, {"text": "it", "type": "Generic"}, {"text": "image", "type": "OtherScientificTerm"}, {"text": "dense depth map", "type": "OtherScientificTerm"}, {"text": "complementary object information", "type": "OtherScientificTerm"}], "relations": [{"head": "it", "tail": "it", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Generic"}, {"head": "image", "tail": "it", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "image", "tail": "dense depth map", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "dense depth map", "tail": "it", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "complementary object information", "tail": "dense depth map", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "It works in real-time , under heavy clutter , does not require a time consuming training stage , and can handle untextured objects .", "entities": [{"text": "It", "type": "Generic"}, {"text": "time consuming training stage", "type": "OtherScientificTerm"}, {"text": "untextured objects", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities .", "entities": [{"text": "It", "type": "Generic"}, {"text": "templates", "type": "OtherScientificTerm"}, {"text": "modalities", "type": "OtherScientificTerm"}, {"text": "commodity hardware", "type": "OtherScientificTerm"}, {"text": "approach", "type": "Generic"}, {"text": "state-of-the-art methods", "type": "Generic"}, {"text": "single modalities", "type": "OtherScientificTerm"}], "relations": [{"head": "templates", "tail": "modalities", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "approach", "tail": "state-of-the-art methods", "label": "COMPARE", "head_type": "Generic", "tail_type": "Generic"}, {"head": "approach", "tail": "single modalities", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "state-of-the-art methods", "tail": "single modalities", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}] |
[[[1, 6], [39, 39]], [[71, 73], [90, 90]], [[103, 110], [115, 115]]] | [["The", "compact", "description", "of", "a", "video", "sequence", "through", "a", "single", "image", "map", "and", "a", "dominant", "motion", "has", "applications", "in", "several", "domains", ",", "including", "video", "browsing", "and", "retrieval", ",", "compression", ",", "mosaicing", ",", "and", "visual", "summarization", "."], ["Building", "such", "a", "representation", "requires", "the", "capability", "to", "register", "all", "the", "frames", "with", "respect", "to", "the", "dominant", "object", "in", "the", "scene", ",", "a", "task", "which", "has", "been", ",", "in", "the", "past", ",", "addressed", "through", "temporally", "localized", "motion", "estimates", "."], ["In", "this", "paper", ",", "we", "show", "how", "the", "lack", "of", "temporal", "consistency", "associated", "with", "such", "estimates", "can", "undermine", "the", "validity", "of", "the", "dominant", "motion", "assumption", ",", "leading", "to", "oscillation", "between", "different", "scene", "interpretations", "and", "poor", "registration", "."], ["To", "avoid", "this", "oscillation", ",", "we", "augment", "the", "motion", "model", "with", "a", "generic", "temporal", "constraint", "which", "increases", "the", "robustness", "against", "competing", "interpretations", ",", "leading", "to", "more", "meaningful", "content", "summarization", "."]] | [[[1, 6, "Task"], [10, 11, "OtherScientificTerm"], [14, 15, "OtherScientificTerm"], [20, 20, "Generic"], [23, 26, "Task"], [28, 28, "Task"], [30, 30, "Task"], [33, 34, "Task"]], [[39, 39, "Generic"], [59, 59, "Generic"], [71, 73, "Method"]], [[83, 86, "OtherScientificTerm"], [85, 86, "OtherScientificTerm"], [90, 90, "Generic"], [94, 99, "OtherScientificTerm"], [97, 99, "OtherScientificTerm"], [103, 110, "OtherScientificTerm"]], [[115, 115, "Generic"], [120, 121, "Method"], [124, 126, "OtherScientificTerm"], [130, 130, "Metric"], [139, 140, "Task"]]] | [[[1, 6, 20, 20, "USED-FOR"], [10, 11, 1, 6, "USED-FOR"], [10, 11, 14, 15, "CONJUNCTION"], [14, 15, 1, 6, "USED-FOR"], [23, 26, 20, 20, "HYPONYM-OF"], [23, 26, 28, 28, "CONJUNCTION"], [28, 28, 20, 20, "HYPONYM-OF"], [28, 28, 30, 30, "CONJUNCTION"], [30, 30, 20, 20, "HYPONYM-OF"], [30, 30, 33, 34, "CONJUNCTION"]], [[71, 73, 59, 59, "USED-FOR"]], [], [[124, 126, 120, 121, "USED-FOR"], [124, 126, 139, 140, "USED-FOR"], [130, 130, 124, 126, "EVALUATE-FOR"]]] | CVPR_1998_10_abs | train | [{"sentence": "The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .", "entities": [{"text": "compact description of a video sequence", "type": "Task"}, {"text": "image map", "type": "OtherScientificTerm"}, {"text": "dominant motion", "type": "OtherScientificTerm"}, {"text": "domains", "type": "Generic"}, {"text": "video browsing and retrieval", "type": "Task"}, {"text": "compression", "type": "Task"}, {"text": "mosaicing", "type": "Task"}, {"text": "visual summarization", "type": "Task"}], "relations": [{"head": "compact description of a video sequence", "tail": "domains", "label": "USED-FOR", "head_type": "Task", "tail_type": "Generic"}, {"head": "image map", "tail": "compact description of a video sequence", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}, {"head": "image map", "tail": "dominant motion", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "dominant motion", "tail": "compact description of a video sequence", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}, {"head": "video browsing and retrieval", "tail": "domains", "label": "HYPONYM-OF", "head_type": "Task", "tail_type": "Generic"}, {"head": "video browsing and retrieval", "tail": "compression", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}, {"head": "compression", "tail": "domains", "label": "HYPONYM-OF", "head_type": "Task", "tail_type": "Generic"}, {"head": "compression", "tail": "mosaicing", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}, {"head": "mosaicing", "tail": "domains", "label": "HYPONYM-OF", "head_type": "Task", "tail_type": "Generic"}, {"head": "mosaicing", "tail": "visual summarization", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a task which has been , in the past , addressed through temporally localized motion estimates .", "entities": [{"text": "representation", "type": "Generic"}, {"text": "task", "type": "Generic"}, {"text": "localized motion estimates", "type": "Method"}], "relations": [{"head": "localized motion estimates", "tail": "task", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}]}, {"sentence": "In this paper , we show how the lack of temporal consistency associated with such estimates can undermine the validity of the dominant motion assumption , leading to oscillation between different scene interpretations and poor registration .", "entities": [{"text": "lack of temporal consistency", "type": "OtherScientificTerm"}, {"text": "temporal consistency", "type": "OtherScientificTerm"}, {"text": "estimates", "type": "Generic"}, {"text": "validity of the dominant motion assumption", "type": "OtherScientificTerm"}, {"text": "dominant motion assumption", "type": "OtherScientificTerm"}, {"text": "oscillation between different scene interpretations and poor registration", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "To avoid this oscillation , we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations , leading to more meaningful content summarization .", "entities": [{"text": "oscillation", "type": "Generic"}, {"text": "motion model", "type": "Method"}, {"text": "generic temporal constraint", "type": "OtherScientificTerm"}, {"text": "robustness", "type": "Metric"}, {"text": "content summarization", "type": "Task"}], "relations": [{"head": "generic temporal constraint", "tail": "motion model", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "generic temporal constraint", "tail": "content summarization", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}, {"head": "robustness", "tail": "generic temporal constraint", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "OtherScientificTerm"}]}] |
[[[60, 64], [166, 166]], [[1, 2], [50, 51], [68, 69]]] | [["In", "cross-domain", "learning", ",", "there", "is", "a", "more", "challenging", "problem", "that", "the", "domain", "divergence", "involves", "more", "than", "one", "dominant", "factors", ",", "e.g.", ",", "different", "viewpoints", ",", "various", "resolutions", "and", "changing", "illuminations", "."], ["Fortunately", ",", "an", "intermediate", "domain", "could", "often", "be", "found", "to", "build", "a", "bridge", "across", "them", "to", "facilitate", "the", "learning", "problem", "."], ["In", "this", "paper", ",", "we", "propose", "a", "Coupled", "Marginalized", "Denoising", "Auto-encoders", "framework", "to", "address", "the", "cross-domain", "problem", "."], ["Specifically", ",", "we", "design", "two", "marginalized", "denoising", "auto-encoders", ",", "one", "for", "the", "target", "and", "the", "other", "for", "source", "as", "well", "as", "the", "intermediate", "one", "."], ["To", "better", "couple", "the", "two", "denoising", "auto-encoders", "learning", ",", "we", "incorporate", "a", "feature", "mapping", ",", "which", "tends", "to", "transfer", "knowledge", "between", "the", "intermediate", "domain", "and", "the", "target", "one", "."], ["Furthermore", ",", "the", "maximum", "margin", "criterion", ",", "e.g.", ",", "intra-class", "com-pactness", "and", "inter-class", "penalty", ",", "on", "the", "output", "layer", "is", "imposed", "to", "seek", "more", "discriminative", "features", "across", "different", "domains", "."], ["Extensive", "experiments", "on", "two", "tasks", "have", "demonstrated", "the", "superiority", "of", "our", "method", "over", "the", "state-of-the-art", "methods", "."]] | [[[1, 2, "Task"], [12, 13, "OtherScientificTerm"], [18, 19, "Generic"], [24, 24, "OtherScientificTerm"], [27, 27, "OtherScientificTerm"], [30, 30, "OtherScientificTerm"]], [[35, 36, "OtherScientificTerm"], [50, 51, "Task"]], [[60, 64, "Method"], [68, 69, "Task"]], [[76, 78, "Method"], [80, 80, "Generic"], [86, 86, "Generic"]], [[101, 103, "Method"], [108, 109, "Method"], [118, 119, "OtherScientificTerm"]], [[128, 130, "OtherScientificTerm"], [134, 135, "OtherScientificTerm"], [137, 138, "OtherScientificTerm"], [149, 150, "OtherScientificTerm"]], [[159, 159, "Generic"], [166, 166, "Generic"], [169, 170, "Generic"]]] | [[[18, 19, 12, 13, "PART-OF"], [24, 24, 18, 19, "HYPONYM-OF"], [24, 24, 27, 27, "CONJUNCTION"], [27, 27, 18, 19, "HYPONYM-OF"], [27, 27, 30, 30, "CONJUNCTION"]], [[35, 36, 50, 51, "USED-FOR"]], [[60, 64, 68, 69, "USED-FOR"]], [[80, 80, 76, 78, "HYPONYM-OF"], [80, 80, 86, 86, "CONJUNCTION"], [86, 86, 76, 78, "HYPONYM-OF"]], [[108, 109, 101, 103, "PART-OF"], [108, 109, 118, 119, "USED-FOR"]], [[134, 135, 128, 130, "HYPONYM-OF"], [134, 135, 137, 138, "CONJUNCTION"], [137, 138, 128, 130, "HYPONYM-OF"]], [[159, 159, 166, 166, "EVALUATE-FOR"], [166, 166, 169, 170, "COMPARE"]]] | IJCAI_2016_420_abs | train | [{"sentence": "In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various resolutions and changing illuminations .", "entities": [{"text": "cross-domain learning", "type": "Task"}, {"text": "domain divergence", "type": "OtherScientificTerm"}, {"text": "dominant factors", "type": "Generic"}, {"text": "viewpoints", "type": "OtherScientificTerm"}, {"text": "resolutions", "type": "OtherScientificTerm"}, {"text": "illuminations", "type": "OtherScientificTerm"}], "relations": [{"head": "dominant factors", "tail": "domain divergence", "label": "PART-OF", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "viewpoints", "tail": "dominant factors", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "viewpoints", "tail": "resolutions", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "resolutions", "tail": "dominant factors", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "resolutions", "tail": "illuminations", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Fortunately , an intermediate domain could often be found to build a bridge across them to facilitate the learning problem .", "entities": [{"text": "intermediate domain", "type": "OtherScientificTerm"}, {"text": "learning problem", "type": "Task"}], "relations": [{"head": "intermediate domain", "tail": "learning problem", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}]}, {"sentence": "In this paper , we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem .", "entities": [{"text": "Coupled Marginalized Denoising Auto-encoders framework", "type": "Method"}, {"text": "cross-domain problem", "type": "Task"}], "relations": [{"head": "Coupled Marginalized Denoising Auto-encoders framework", "tail": "cross-domain problem", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}]}, {"sentence": "Specifically , we design two marginalized denoising auto-encoders , one for the target and the other for source as well as the intermediate one .", "entities": [{"text": "marginalized denoising auto-encoders", "type": "Method"}, {"text": "one", "type": "Generic"}, {"text": "other", "type": "Generic"}], "relations": [{"head": "one", "tail": "marginalized denoising auto-encoders", "label": "HYPONYM-OF", "head_type": "Generic", "tail_type": "Method"}, {"head": "one", "tail": "other", "label": "CONJUNCTION", "head_type": "Generic", "tail_type": "Generic"}, {"head": "other", "tail": "marginalized denoising auto-encoders", "label": "HYPONYM-OF", "head_type": "Generic", "tail_type": "Method"}]}, {"sentence": "To better couple the two denoising auto-encoders learning , we incorporate a feature mapping , which tends to transfer knowledge between the intermediate domain and the target one .", "entities": [{"text": "denoising auto-encoders learning", "type": "Method"}, {"text": "feature mapping", "type": "Method"}, {"text": "intermediate domain", "type": "OtherScientificTerm"}], "relations": [{"head": "feature mapping", "tail": "denoising auto-encoders learning", "label": "PART-OF", "head_type": "Method", "tail_type": "Method"}, {"head": "feature mapping", "tail": "intermediate domain", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Furthermore , the maximum margin criterion , e.g. , intra-class com-pactness and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains .", "entities": [{"text": "maximum margin criterion", "type": "OtherScientificTerm"}, {"text": "intra-class com-pactness", "type": "OtherScientificTerm"}, {"text": "inter-class penalty", "type": "OtherScientificTerm"}, {"text": "discriminative features", "type": "OtherScientificTerm"}], "relations": [{"head": "intra-class com-pactness", "tail": "maximum margin criterion", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "intra-class com-pactness", "tail": "inter-class penalty", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "inter-class penalty", "tail": "maximum margin criterion", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Extensive experiments on two tasks have demonstrated the superiority of our method over the state-of-the-art methods .", "entities": [{"text": "tasks", "type": "Generic"}, {"text": "method", "type": "Generic"}, {"text": "state-of-the-art methods", "type": "Generic"}], "relations": [{"head": "tasks", "tail": "method", "label": "EVALUATE-FOR", "head_type": "Generic", "tail_type": "Generic"}, {"head": "method", "tail": "state-of-the-art methods", "label": "COMPARE", "head_type": "Generic", "tail_type": "Generic"}]}] |
[[[21, 23], [83, 84], [152, 152]], [[43, 45], [57, 57]], [[61, 63], [103, 104]]] | [["In", "this", "paper", ",", "we", "aim", "to", "automatically", "render", "aging", "faces", "in", "a", "personalized", "way", "."], ["Basically", ",", "a", "set", "of", "age-group", "specific", "dictionaries", "are", "learned", ",", "where", "the", "dictionary", "bases", "corresponding", "to", "the", "same", "index", "yet", "from", "different", "dictionaries", "form", "a", "particular", "aging", "process", "pattern", "cross", "different", "age", "groups", ",", "and", "a", "linear", "combination", "of", "these", "patterns", "expresses", "a", "particular", "personalized", "aging", "process", "."], ["Moreover", ",", "two", "factors", "are", "taken", "into", "consideration", "in", "the", "dictionary", "learning", "process", "."], ["First", ",", "beyond", "the", "aging", "dictionaries", ",", "each", "subject", "may", "have", "extra", "personalized", "facial", "characteristics", ",", "e.g.", "mole", ",", "which", "are", "invariant", "in", "the", "aging", "process", "."], ["Second", ",", "it", "is", "challenging", "or", "even", "impossible", "to", "collect", "faces", "of", "all", "age", "groups", "for", "a", "particular", "subject", ",", "yet", "much", "easier", "and", "more", "practical", "to", "get", "face", "pairs", "from", "neighboring", "age", "groups", "."], ["Thus", "a", "personality-aware", "coupled", "reconstruction", "loss", "is", "utilized", "to", "learn", "the", "dictionaries", "based", "on", "face", "pairs", "from", "neighboring", "age", "groups", "."], ["Extensive", "experiments", "well", "demonstrate", "the", "advantages", "of", "our", "proposed", "solution", "over", "other", "state-of-the-arts", "in", "term", "of", "personalized", "aging", "progression", ",", "as", "well", "as", "the", "performance", "gain", "for", "cross-age", "face", "verification", "by", "synthesizing", "aging", "faces", "."]] | [[[9, 10, "OtherScientificTerm"]], [[21, 23, "Method"], [29, 30, "OtherScientificTerm"], [43, 45, "OtherScientificTerm"], [53, 54, "Method"], [57, 57, "Generic"], [61, 63, "Task"]], [[75, 77, "Method"]], [[83, 84, "Method"], [91, 93, "OtherScientificTerm"], [96, 96, "OtherScientificTerm"], [103, 104, "Task"]], [], [[143, 146, "OtherScientificTerm"], [152, 152, "Generic"]], [[171, 171, "Generic"], [174, 174, "Generic"], [178, 180, "OtherScientificTerm"], [189, 191, "Task"], [193, 195, "Method"]]] | [[], [[29, 30, 21, 23, "PART-OF"], [53, 54, 61, 63, "USED-FOR"], [57, 57, 53, 54, "USED-FOR"]], [], [[96, 96, 91, 93, "HYPONYM-OF"]], [], [[143, 146, 152, 152, "USED-FOR"]], [[171, 171, 174, 174, "COMPARE"], [171, 171, 178, 180, "USED-FOR"], [174, 174, 178, 180, "USED-FOR"], [193, 195, 189, 191, "USED-FOR"]]] | ICCV_2015_403_abs | train | [{"sentence": "In this paper , we aim to automatically render aging faces in a personalized way .", "entities": [{"text": "aging faces", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a linear combination of these patterns expresses a particular personalized aging process .", "entities": [{"text": "age-group specific dictionaries", "type": "Method"}, {"text": "dictionary bases", "type": "OtherScientificTerm"}, {"text": "aging process pattern", "type": "OtherScientificTerm"}, {"text": "linear combination", "type": "Method"}, {"text": "patterns", "type": "Generic"}, {"text": "personalized aging process", "type": "Task"}], "relations": [{"head": "dictionary bases", "tail": "age-group specific dictionaries", "label": "PART-OF", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "linear combination", "tail": "personalized aging process", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "patterns", "tail": "linear combination", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Method"}]}, {"sentence": "Moreover , two factors are taken into consideration in the dictionary learning process .", "entities": [{"text": "dictionary learning process", "type": "Method"}], "relations": []}, {"sentence": "First , beyond the aging dictionaries , each subject may have extra personalized facial characteristics , e.g. mole , which are invariant in the aging process .", "entities": [{"text": "aging dictionaries", "type": "Method"}, {"text": "personalized facial characteristics", "type": "OtherScientificTerm"}, {"text": "mole", "type": "OtherScientificTerm"}, {"text": "aging process", "type": "Task"}], "relations": [{"head": "mole", "tail": "personalized facial characteristics", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Second , it is challenging or even impossible to collect faces of all age groups for a particular subject , yet much easier and more practical to get face pairs from neighboring age groups .", "entities": [], "relations": []}, {"sentence": "Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups .", "entities": [{"text": "personality-aware coupled reconstruction loss", "type": "OtherScientificTerm"}, {"text": "dictionaries", "type": "Generic"}], "relations": [{"head": "personality-aware coupled reconstruction loss", "tail": "dictionaries", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}]}, {"sentence": "Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression , as well as the performance gain for cross-age face verification by synthesizing aging faces .", "entities": [{"text": "solution", "type": "Generic"}, {"text": "state-of-the-arts", "type": "Generic"}, {"text": "personalized aging progression", "type": "OtherScientificTerm"}, {"text": "cross-age face verification", "type": "Task"}, {"text": "synthesizing aging faces", "type": "Method"}], "relations": [{"head": "solution", "tail": "state-of-the-arts", "label": "COMPARE", "head_type": "Generic", "tail_type": "Generic"}, {"head": "solution", "tail": "personalized aging progression", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "state-of-the-arts", "tail": "personalized aging progression", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "synthesizing aging faces", "tail": "cross-age face verification", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}]}] |
[] | [["We", "propose", "a", "draft", "scheme", "of", "the", "model", "formalizing", "the", "structure", "of", "communicative", "context", "in", "dialogue", "interaction", "."], ["The", "relationships", "between", "the", "interacting", "partners", "are", "considered", "as", "system", "of", "three", "automata", "representing", "the", "partners", "of", "the", "dialogue", "and", "environment", "."]] | [[[7, 7, "Generic"], [10, 13, "OtherScientificTerm"], [15, 16, "Material"]], []] | [[[7, 7, 10, 13, "USED-FOR"], [15, 16, 10, 13, "FEATURE-OF"]], []] | E85-1041 | train | [{"sentence": "We propose a draft scheme of the model formalizing the structure of communicative context in dialogue interaction .", "entities": [{"text": "model", "type": "Generic"}, {"text": "structure of communicative context", "type": "OtherScientificTerm"}, {"text": "dialogue interaction", "type": "Material"}], "relations": [{"head": "model", "tail": "structure of communicative context", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "dialogue interaction", "tail": "structure of communicative context", "label": "FEATURE-OF", "head_type": "Material", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The relationships between the interacting partners are considered as system of three automata representing the partners of the dialogue and environment .", "entities": [], "relations": []}] |
[[[82, 82], [108, 109]], [[56, 56], [58, 58], [75, 75], [129, 129]]] | [["Visitors", "who", "browse", "the", "web", "from", "wireless", "PDAs", ",", "cell", "phones", ",", "and", "pagers", "are", "frequently", "stymied", "by", "web", "interfaces", "optimized", "for", "desktop", "PCs", "."], ["Simply", "replacing", "graphics", "with", "text", "and", "reformatting", "tables", "does", "not", "solve", "the", "problem", ",", "because", "deep", "link", "structures", "can", "still", "require", "minutes", "to", "traverse", "."], ["In", "this", "paper", "we", "develop", "an", "algorithm", ",", "MINPATH", ",", "that", "automatically", "improves", "wireless", "web", "navigation", "by", "suggesting", "useful", "shortcut", "links", "in", "real", "time", "."], ["MINPATH", "finds", "shortcuts", "by", "using", "a", "learned", "model", "of", "web", "visitor", "behavior", "to", "estimate", "the", "savings", "of", "shortcut", "links", ",", "and", "suggests", "only", "the", "few", "best", "links", "."], ["We", "explore", "a", "variety", "of", "predictive", "models", ",", "including", "Na", "\u00a8", "\u0131ve", "Bayes", "mixture", "models", "and", "mixtures", "of", "Markov", "models", ",", "and", "report", "empirical", "evidence", "that", "MINPATH", "finds", "useful", "shortcuts", "that", "save", "substantial", "navigational", "effort", "."]] | [[[18, 19, "Method"], [22, 23, "OtherScientificTerm"]], [[40, 42, "OtherScientificTerm"]], [[56, 56, "Generic"], [58, 58, "Method"], [63, 65, "Task"]], [[75, 75, "Method"], [82, 82, "Generic"], [84, 86, "OtherScientificTerm"], [90, 93, "Task"]], [[108, 109, "Generic"], [112, 117, "Method"], [119, 122, "Method"], [129, 129, "Method"]]] | [[[18, 19, 22, 23, "USED-FOR"]], [], [[56, 56, 63, 65, "USED-FOR"], [56, 56, 63, 65, "USED-FOR"]], [[82, 82, 75, 75, "USED-FOR"], [82, 82, 84, 86, "USED-FOR"], [82, 82, 90, 93, "USED-FOR"]], [[112, 117, 108, 109, "HYPONYM-OF"], [112, 117, 119, 122, "CONJUNCTION"], [119, 122, 108, 109, "HYPONYM-OF"]]] | IJCAI_2001_12_abs | train | [{"sentence": "Visitors who browse the web from wireless PDAs , cell phones , and pagers are frequently stymied by web interfaces optimized for desktop PCs .", "entities": [{"text": "web interfaces", "type": "Method"}, {"text": "desktop PCs", "type": "OtherScientificTerm"}], "relations": [{"head": "web interfaces", "tail": "desktop PCs", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Simply replacing graphics with text and reformatting tables does not solve the problem , because deep link structures can still require minutes to traverse .", "entities": [{"text": "deep link structures", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "In this paper we develop an algorithm , MINPATH , that automatically improves wireless web navigation by suggesting useful shortcut links in real time .", "entities": [{"text": "algorithm", "type": "Generic"}, {"text": "MINPATH", "type": "Method"}, {"text": "wireless web navigation", "type": "Task"}], "relations": [{"head": "algorithm", "tail": "wireless web navigation", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "algorithm", "tail": "wireless web navigation", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}, {"sentence": "MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links , and suggests only the few best links .", "entities": [{"text": "MINPATH", "type": "Method"}, {"text": "model", "type": "Generic"}, {"text": "web visitor behavior", "type": "OtherScientificTerm"}, {"text": "savings of shortcut links", "type": "Task"}], "relations": [{"head": "model", "tail": "MINPATH", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Method"}, {"head": "model", "tail": "web visitor behavior", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "model", "tail": "savings of shortcut links", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}, {"sentence": "We explore a variety of predictive models , including Na \u00a8 \u0131ve Bayes mixture models and mixtures of Markov models , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .", "entities": [{"text": "predictive models", "type": "Generic"}, {"text": "Na \u00a8 \u0131ve Bayes mixture models", "type": "Method"}, {"text": "mixtures of Markov models", "type": "Method"}, {"text": "MINPATH", "type": "Method"}], "relations": [{"head": "Na \u00a8 \u0131ve Bayes mixture models", "tail": "predictive models", "label": "HYPONYM-OF", "head_type": "Method", "tail_type": "Generic"}, {"head": "Na \u00a8 \u0131ve Bayes mixture models", "tail": "mixtures of Markov models", "label": "CONJUNCTION", "head_type": "Method", "tail_type": "Method"}, {"head": "mixtures of Markov models", "tail": "predictive models", "label": "HYPONYM-OF", "head_type": "Method", "tail_type": "Generic"}]}] |
[[[48, 51], [121, 123]], [[22, 24], [35, 35]], [[5, 5], [84, 84]], [[108, 108], [125, 125], [142, 142]]] | [["This", "paper", "describes", "a", "particular", "approach", "to", "parsing", "that", "utilizes", "recent", "advances", "in", "unification-based", "parsing", "and", "in", "classification-based", "knowledge", "representation", "."], ["As", "unification-based", "grammatical", "frameworks", "are", "extended", "to", "handle", "richer", "descriptions", "of", "linguistic", "information", ",", "they", "begin", "to", "share", "many", "of", "the", "properties", "that", "have", "been", "developed", "in", "KL-ONE-like", "knowledge", "representation", "systems", "."], ["This", "commonality", "suggests", "that", "some", "of", "the", "classification-based", "representation", "techniques", "can", "be", "applied", "to", "unification-based", "linguistic", "descriptions", "."], ["This", "merging", "supports", "the", "integration", "of", "semantic", "and", "syntactic", "information", "into", "the", "same", "system", ",", "simultaneously", "subject", "to", "the", "same", "types", "of", "processes", ",", "in", "an", "efficient", "manner", "."], ["The", "result", "is", "expected", "to", "be", "more", "efficient", "parsing", "due", "to", "the", "increased", "organization", "of", "knowledge", "."], ["The", "use", "of", "a", "KL-ONE", "style", "representation", "for", "parsing", "and", "semantic", "interpretation", "was", "first", "explored", "in", "the", "PSI-KLONE", "system", "-LSB-", "2", "-RSB-", ",", "in", "which", "parsing", "is", "characterized", "as", "an", "inference", "process", "called", "incremental", "description", "refinement", "."]] | [[[5, 5, "Generic"], [7, 7, "Task"], [13, 14, "Task"], [17, 19, "Task"]], [[22, 24, "Method"], [32, 33, "OtherScientificTerm"], [35, 35, "Generic"], [48, 51, "Method"]], [[60, 62, "Method"], [67, 69, "OtherScientificTerm"]], [[77, 80, "OtherScientificTerm"], [84, 84, "Generic"]], [[108, 108, "Method"]], [[121, 123, "Method"], [125, 125, "Task"], [127, 128, "Task"], [134, 135, "Method"], [142, 142, "Task"], [147, 148, "Method"], [150, 152, "Method"]]] | [[[5, 5, 7, 7, "USED-FOR"], [13, 14, 5, 5, "USED-FOR"], [17, 19, 5, 5, "USED-FOR"], [17, 19, 13, 14, "CONJUNCTION"]], [[22, 24, 32, 33, "USED-FOR"], [48, 51, 35, 35, "USED-FOR"]], [[60, 62, 67, 69, "USED-FOR"]], [[77, 80, 84, 84, "USED-FOR"]], [], [[121, 123, 125, 125, "USED-FOR"], [121, 123, 127, 128, "USED-FOR"], [125, 125, 127, 128, "CONJUNCTION"], [134, 135, 121, 123, "USED-FOR"], [150, 152, 142, 142, "USED-FOR"], [150, 152, 147, 148, "HYPONYM-OF"]]] | H90-1011 | train | [{"sentence": "This paper describes a particular approach to parsing that utilizes recent advances in unification-based parsing and in classification-based knowledge representation .", "entities": [{"text": "approach", "type": "Generic"}, {"text": "parsing", "type": "Task"}, {"text": "unification-based parsing", "type": "Task"}, {"text": "classification-based knowledge representation", "type": "Task"}], "relations": [{"head": "approach", "tail": "parsing", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "unification-based parsing", "tail": "approach", "label": "USED-FOR", "head_type": "Task", "tail_type": "Generic"}, {"head": "classification-based knowledge representation", "tail": "approach", "label": "USED-FOR", "head_type": "Task", "tail_type": "Generic"}, {"head": "classification-based knowledge representation", "tail": "unification-based parsing", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information , they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems .", "entities": [{"text": "unification-based grammatical frameworks", "type": "Method"}, {"text": "linguistic information", "type": "OtherScientificTerm"}, {"text": "they", "type": "Generic"}, {"text": "KL-ONE-like knowledge representation systems", "type": "Method"}], "relations": [{"head": "unification-based grammatical frameworks", "tail": "linguistic information", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "KL-ONE-like knowledge representation systems", "tail": "they", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}]}, {"sentence": "This commonality suggests that some of the classification-based representation techniques can be applied to unification-based linguistic descriptions .", "entities": [{"text": "classification-based representation techniques", "type": "Method"}, {"text": "unification-based linguistic descriptions", "type": "OtherScientificTerm"}], "relations": [{"head": "classification-based representation techniques", "tail": "unification-based linguistic descriptions", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "This merging supports the integration of semantic and syntactic information into the same system , simultaneously subject to the same types of processes , in an efficient manner .", "entities": [{"text": "semantic and syntactic information", "type": "OtherScientificTerm"}, {"text": "system", "type": "Generic"}], "relations": [{"head": "semantic and syntactic information", "tail": "system", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}]}, {"sentence": "The result is expected to be more efficient parsing due to the increased organization of knowledge .", "entities": [{"text": "parsing", "type": "Method"}], "relations": []}, {"sentence": "The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .", "entities": [{"text": "KL-ONE style representation", "type": "Method"}, {"text": "parsing", "type": "Task"}, {"text": "semantic interpretation", "type": "Task"}, {"text": "PSI-KLONE system", "type": "Method"}, {"text": "parsing", "type": "Task"}, {"text": "inference process", "type": "Method"}, {"text": "incremental description refinement", "type": "Method"}], "relations": [{"head": "KL-ONE style representation", "tail": "parsing", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "KL-ONE style representation", "tail": "semantic interpretation", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "parsing", "tail": "semantic interpretation", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}, {"head": "PSI-KLONE system", "tail": "KL-ONE style representation", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}, {"head": "incremental description refinement", "tail": "parsing", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "incremental description refinement", "tail": "inference process", "label": "HYPONYM-OF", "head_type": "Method", "tail_type": "Method"}]}] |
[[[7, 10], [28, 28], [54, 55], [76, 76], [84, 84]]] | [["In", "this", "paper", "we", "discuss", "a", "proposed", "user", "knowledge", "modeling", "architecture", "for", "the", "ICICLE", "system", ",", "a", "language", "tutoring", "application", "for", "deaf", "learners", "of", "written", "English", "."], ["The", "model", "will", "represent", "the", "language", "proficiency", "of", "the", "user", "and", "is", "designed", "to", "be", "referenced", "during", "both", "writing", "analysis", "and", "feedback", "production", "."], ["We", "motivate", "our", "model", "design", "by", "citing", "relevant", "research", "on", "second", "language", "and", "cognitive", "skill", "acquisition", ",", "and", "briefly", "discuss", "preliminary", "empirical", "evidence", "supporting", "the", "design", "."], ["We", "conclude", "by", "showing", "how", "our", "design", "can", "provide", "a", "rich", "and", "robust", "information", "base", "to", "a", "language", "assessment", "/", "correction", "application", "by", "modeling", "user", "proficiency", "at", "a", "high", "level", "of", "granularity", "and", "specificity", "."]] | [[[7, 10, "Method"], [13, 14, "Task"], [17, 19, "Task"], [21, 22, "OtherScientificTerm"], [24, 25, "Material"]], [[28, 28, "Generic"], [45, 46, "Task"], [48, 49, "Task"]], [[54, 55, "Generic"], [61, 66, "Task"], [76, 76, "Generic"]], [[84, 84, "Generic"], [95, 99, "Task"], [102, 103, "OtherScientificTerm"], [109, 109, "Metric"], [111, 111, "Metric"]]] | [[[7, 10, 13, 14, "USED-FOR"], [13, 14, 17, 19, "HYPONYM-OF"], [17, 19, 21, 22, "USED-FOR"], [24, 25, 17, 19, "USED-FOR"]], [[28, 28, 45, 46, "USED-FOR"], [28, 28, 48, 49, "USED-FOR"], [45, 46, 48, 49, "CONJUNCTION"]], [[61, 66, 54, 55, "USED-FOR"]], [[84, 84, 95, 99, "USED-FOR"], [84, 84, 102, 103, "USED-FOR"], [109, 109, 102, 103, "EVALUATE-FOR"], [109, 109, 111, 111, "CONJUNCTION"], [111, 111, 102, 103, "EVALUATE-FOR"]]] | W99-0408 | train | [{"sentence": "In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a language tutoring application for deaf learners of written English .", "entities": [{"text": "user knowledge modeling architecture", "type": "Method"}, {"text": "ICICLE system", "type": "Task"}, {"text": "language tutoring application", "type": "Task"}, {"text": "deaf learners", "type": "OtherScientificTerm"}, {"text": "written English", "type": "Material"}], "relations": [{"head": "user knowledge modeling architecture", "tail": "ICICLE system", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "ICICLE system", "tail": "language tutoring application", "label": "HYPONYM-OF", "head_type": "Task", "tail_type": "Task"}, {"head": "language tutoring application", "tail": "deaf learners", "label": "USED-FOR", "head_type": "Task", "tail_type": "OtherScientificTerm"}, {"head": "written English", "tail": "language tutoring application", "label": "USED-FOR", "head_type": "Material", "tail_type": "Task"}]}, {"sentence": "The model will represent the language proficiency of the user and is designed to be referenced during both writing analysis and feedback production .", "entities": [{"text": "model", "type": "Generic"}, {"text": "writing analysis", "type": "Task"}, {"text": "feedback production", "type": "Task"}], "relations": [{"head": "model", "tail": "writing analysis", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "model", "tail": "feedback production", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "writing analysis", "tail": "feedback production", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "We motivate our model design by citing relevant research on second language and cognitive skill acquisition , and briefly discuss preliminary empirical evidence supporting the design .", "entities": [{"text": "model design", "type": "Generic"}, {"text": "second language and cognitive skill acquisition", "type": "Task"}, {"text": "design", "type": "Generic"}], "relations": [{"head": "second language and cognitive skill acquisition", "tail": "model design", "label": "USED-FOR", "head_type": "Task", "tail_type": "Generic"}]}, {"sentence": "We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of granularity and specificity .", "entities": [{"text": "design", "type": "Generic"}, {"text": "language assessment / correction application", "type": "Task"}, {"text": "user proficiency", "type": "OtherScientificTerm"}, {"text": "granularity", "type": "Metric"}, {"text": "specificity", "type": "Metric"}], "relations": [{"head": "design", "tail": "language assessment / correction application", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "design", "tail": "user proficiency", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "granularity", "tail": "user proficiency", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "OtherScientificTerm"}, {"head": "granularity", "tail": "specificity", "label": "CONJUNCTION", "head_type": "Metric", "tail_type": "Metric"}, {"head": "specificity", "tail": "user proficiency", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "OtherScientificTerm"}]}] |
[[[116, 116], [132, 132], [176, 176], [196, 196]], [[122, 127], [183, 184], [219, 220]]] | [["Constraint", "propagation", "is", "one", "of", "the", "key", "techniques", "in", "constraint", "programming", ",", "and", "a", "large", "body", "of", "work", "has", "built", "up", "around", "it", "."], ["Special-purpose", "constraint", "propagation", "algorithms", "frequently", "make", "implicit", "use", "of", "short", "supports", "--", "by", "examining", "a", "subset", "of", "the", "variables", ",", "they", "can", "infer", "support", "-LRB-", "a", "justification", "that", "a", "variable-value", "pair", "still", "forms", "part", "of", "a", "solution", "to", "the", "constraint", "-RRB-", "for", "all", "other", "variables", "and", "values", "and", "save", "substantial", "work", "."], ["Recently", "short", "supports", "have", "been", "used", "in", "general", "purpose", "prop-agators", ",", "and", "-LRB-", "when", "the", "constraint", "is", "amenable", "to", "short", "supports", "-RRB-", "speed", "ups", "of", "more", "than", "three", "orders", "of", "magnitude", "have", "been", "demonstrated", "."], ["In", "this", "paper", "we", "present", "SHORTSTR2", ",", "a", "development", "of", "the", "Simple", "Tabular", "Reduction", "algorithm", "STR2", "+", "."], ["We", "show", "that", "SHORTSTR2", "is", "complementary", "to", "the", "existing", "algorithms", "SHORTGAC", "and", "HAGGISGAC", "that", "exploit", "short", "supports", ",", "while", "being", "much", "simpler", "."], ["When", "a", "constraint", "is", "amenable", "to", "short", "supports", ",", "the", "short", "support", "set", "can", "be", "exponentially", "smaller", "than", "the", "full-length", "support", "set", "."], ["Therefore", "SHORTSTR2", "can", "efficiently", "propagate", "many", "constraints", "that", "STR2", "+", "can", "not", "even", "load", "into", "memory", "."], ["We", "also", "show", "that", "SHORTSTR2", "can", "be", "combined", "with", "a", "simple", "algorithm", "to", "identify", "short", "supports", "from", "full-length", "supports", ",", "to", "provide", "a", "superior", "drop-in", "replacement", "for", "STR2", "+", "."]] | [[[0, 1, "Method"], [9, 10, "Method"]], [[24, 27, "Method"]], [[83, 85, "Method"], [91, 91, "OtherScientificTerm"]], [[116, 116, "Method"], [122, 127, "Method"]], [[132, 132, "Method"], [139, 139, "Method"], [141, 141, "Method"]], [[154, 154, "OtherScientificTerm"], [158, 159, "OtherScientificTerm"], [162, 164, "OtherScientificTerm"], [171, 173, "OtherScientificTerm"]], [[176, 176, "Method"], [181, 181, "OtherScientificTerm"], [183, 184, "Method"]], [[196, 196, "Method"], [203, 203, "Generic"], [206, 207, "OtherScientificTerm"], [209, 210, "OtherScientificTerm"], [216, 217, "OtherScientificTerm"], [219, 220, "Method"]]] | [[[0, 1, 9, 10, "PART-OF"]], [], [], [[122, 127, 116, 116, "USED-FOR"]], [[132, 132, 139, 139, "COMPARE"], [132, 132, 141, 141, "COMPARE"], [139, 139, 141, 141, "CONJUNCTION"]], [[162, 164, 171, 173, "COMPARE"]], [], [[196, 196, 206, 207, "USED-FOR"], [196, 196, 216, 217, "USED-FOR"], [203, 203, 196, 196, "CONJUNCTION"], [203, 203, 206, 207, "USED-FOR"], [209, 210, 196, 196, "USED-FOR"], [209, 210, 196, 196, "USED-FOR"], [209, 210, 203, 203, "USED-FOR"], [209, 210, 203, 203, "USED-FOR"], [216, 217, 219, 220, "USED-FOR"]]] | IJCAI_2013_5_abs | train | [{"sentence": "Constraint propagation is one of the key techniques in constraint programming , and a large body of work has built up around it .", "entities": [{"text": "Constraint propagation", "type": "Method"}, {"text": "constraint programming", "type": "Method"}], "relations": [{"head": "Constraint propagation", "tail": "constraint programming", "label": "PART-OF", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "Special-purpose constraint propagation algorithms frequently make implicit use of short supports -- by examining a subset of the variables , they can infer support -LRB- a justification that a variable-value pair still forms part of a solution to the constraint -RRB- for all other variables and values and save substantial work .", "entities": [{"text": "Special-purpose constraint propagation algorithms", "type": "Method"}], "relations": []}, {"sentence": "Recently short supports have been used in general purpose prop-agators , and -LRB- when the constraint is amenable to short supports -RRB- speed ups of more than three orders of magnitude have been demonstrated .", "entities": [{"text": "general purpose prop-agators", "type": "Method"}, {"text": "constraint", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "In this paper we present SHORTSTR2 , a development of the Simple Tabular Reduction algorithm STR2 + .", "entities": [{"text": "SHORTSTR2", "type": "Method"}, {"text": "Simple Tabular Reduction algorithm STR2 +", "type": "Method"}], "relations": [{"head": "Simple Tabular Reduction algorithm STR2 +", "tail": "SHORTSTR2", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "We show that SHORTSTR2 is complementary to the existing algorithms SHORTGAC and HAGGISGAC that exploit short supports , while being much simpler .", "entities": [{"text": "SHORTSTR2", "type": "Method"}, {"text": "SHORTGAC", "type": "Method"}, {"text": "HAGGISGAC", "type": "Method"}], "relations": [{"head": "SHORTSTR2", "tail": "SHORTGAC", "label": "COMPARE", "head_type": "Method", "tail_type": "Method"}, {"head": "SHORTSTR2", "tail": "HAGGISGAC", "label": "COMPARE", "head_type": "Method", "tail_type": "Method"}, {"head": "SHORTGAC", "tail": "HAGGISGAC", "label": "CONJUNCTION", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "When a constraint is amenable to short supports , the short support set can be exponentially smaller than the full-length support set .", "entities": [{"text": "constraint", "type": "OtherScientificTerm"}, {"text": "short supports", "type": "OtherScientificTerm"}, {"text": "short support set", "type": "OtherScientificTerm"}, {"text": "full-length support set", "type": "OtherScientificTerm"}], "relations": [{"head": "short support set", "tail": "full-length support set", "label": "COMPARE", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Therefore SHORTSTR2 can efficiently propagate many constraints that STR2 + can not even load into memory .", "entities": [{"text": "SHORTSTR2", "type": "Method"}, {"text": "constraints", "type": "OtherScientificTerm"}, {"text": "STR2 +", "type": "Method"}], "relations": []}, {"sentence": "We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior drop-in replacement for STR2 + .", "entities": [{"text": "SHORTSTR2", "type": "Method"}, {"text": "algorithm", "type": "Generic"}, {"text": "short supports", "type": "OtherScientificTerm"}, {"text": "full-length supports", "type": "OtherScientificTerm"}, {"text": "drop-in replacement", "type": "OtherScientificTerm"}, {"text": "STR2 +", "type": "Method"}], "relations": [{"head": "SHORTSTR2", "tail": "short supports", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "SHORTSTR2", "tail": "drop-in replacement", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "algorithm", "tail": "SHORTSTR2", "label": "CONJUNCTION", "head_type": "Generic", "tail_type": "Method"}, {"head": "algorithm", "tail": "short supports", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "full-length supports", "tail": "SHORTSTR2", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "full-length supports", "tail": "SHORTSTR2", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "full-length supports", "tail": "algorithm", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "full-length supports", "tail": "algorithm", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "drop-in replacement", "tail": "STR2 +", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}] |
[[[3, 4], [17, 17], [47, 47]]] | [["We", "propose", "a", "detection", "method", "for", "orthographic", "variants", "caused", "by", "transliteration", "in", "a", "large", "corpus", "."], ["The", "method", "employs", "two", "similarities", "."], ["One", "is", "string", "similarity", "based", "on", "edit", "distance", "."], ["The", "other", "is", "contextual", "similarity", "by", "a", "vector", "space", "model", "."], ["Experimental", "results", "show", "that", "the", "method", "performed", "a", "0.889", "F-measure", "in", "an", "open", "test", "."]] | [[[3, 4, "Method"], [6, 7, "OtherScientificTerm"], [10, 10, "Task"]], [[17, 17, "Generic"], [20, 20, "Generic"]], [[24, 25, "OtherScientificTerm"], [28, 29, "Method"]], [[34, 35, "OtherScientificTerm"], [38, 40, "Method"]], [[47, 47, "Generic"], [51, 51, "Metric"]]] | [[[3, 4, 6, 7, "USED-FOR"]], [[20, 20, 17, 17, "USED-FOR"]], [[28, 29, 24, 25, "USED-FOR"]], [[38, 40, 34, 35, "USED-FOR"]], [[51, 51, 47, 47, "EVALUATE-FOR"]]] | C04-1102 | train | [{"sentence": "We propose a detection method for orthographic variants caused by transliteration in a large corpus .", "entities": [{"text": "detection method", "type": "Method"}, {"text": "orthographic variants", "type": "OtherScientificTerm"}, {"text": "transliteration", "type": "Task"}], "relations": [{"head": "detection method", "tail": "orthographic variants", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The method employs two similarities .", "entities": [{"text": "method", "type": "Generic"}, {"text": "similarities", "type": "Generic"}], "relations": [{"head": "similarities", "tail": "method", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Generic"}]}, {"sentence": "One is string similarity based on edit distance .", "entities": [{"text": "string similarity", "type": "OtherScientificTerm"}, {"text": "edit distance", "type": "Method"}], "relations": [{"head": "edit distance", "tail": "string similarity", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The other is contextual similarity by a vector space model .", "entities": [{"text": "contextual similarity", "type": "OtherScientificTerm"}, {"text": "vector space model", "type": "Method"}], "relations": [{"head": "vector space model", "tail": "contextual similarity", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Experimental results show that the method performed a 0.889 F-measure in an open test .", "entities": [{"text": "method", "type": "Generic"}, {"text": "F-measure", "type": "Metric"}], "relations": [{"head": "F-measure", "tail": "method", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Generic"}]}] |
[[[16, 25], [46, 46]], [[7, 8], [29, 29], [160, 160]], [[77, 78], [139, 139]], [[59, 61], [74, 74]]] | [["Uncertainty", "handling", "plays", "an", "important", "role", "during", "shape", "tracking", "."], ["We", "have", "recently", "shown", "that", "the", "fusion", "of", "measurement", "information", "with", "system", "dynamics", "and", "shape", "priors", "greatly", "improves", "the", "tracking", "performance", "for", "very", "noisy", "images", "such", "as", "ultrasound", "sequences", "-LSB-", "22", "-RSB-", "."], ["Nevertheless", ",", "this", "approach", "required", "user", "initialization", "of", "the", "tracking", "process", "."], ["This", "paper", "solves", "the", "automatic", "initial-ization", "problem", "by", "performing", "boosted", "shape", "detection", "as", "a", "generic", "measurement", "process", "and", "integrating", "it", "in", "our", "tracking", "framework", "."], ["We", "show", "how", "to", "propagate", "the", "local", "detection", "uncertainties", "of", "multiple", "shape", "candidates", "during", "shape", "alignment", ",", "fusion", "with", "the", "predicted", "shape", "prior", ",", "and", "fusion", "with", "subspace", "constraints", "."], ["As", "a", "result", ",", "we", "treat", "all", "sources", "of", "information", "in", "a", "unified", "way", "and", "derive", "the", "posterior", "shape", "model", "as", "the", "shape", "with", "the", "maximum", "likelihood", "."], ["Our", "framework", "is", "applied", "for", "the", "automatic", "tracking", "of", "endocardium", "in", "ultrasound", "sequences", "of", "the", "human", "heart", "."], ["Reliable", "detection", "and", "robust", "tracking", "results", "are", "achieved", "when", "compared", "to", "existing", "approaches", "and", "inter-expert", "variations", "."]] | [[[0, 1, "Task"], [7, 8, "Task"]], [[16, 25, "Method"], [29, 29, "Task"], [33, 34, "Material"], [37, 38, "Material"]], [[46, 46, "Generic"], [48, 49, "OtherScientificTerm"], [52, 53, "Method"]], [[59, 61, "Task"], [64, 66, "Method"], [69, 71, "Method"], [74, 74, "Generic"], [77, 78, "Method"]], [[86, 88, "OtherScientificTerm"], [86, 92, "OtherScientificTerm"], [94, 95, "OtherScientificTerm"], [100, 102, "OtherScientificTerm"], [107, 108, "OtherScientificTerm"]], [[127, 129, "Method"], [135, 136, "OtherScientificTerm"]], [[139, 139, "Generic"], [144, 147, "Task"], [147, 147, "OtherScientificTerm"], [149, 154, "Material"]], [[157, 157, "Task"], [160, 160, "Task"], [168, 168, "Generic"], [170, 171, "OtherScientificTerm"]]] | [[[0, 1, 7, 8, "USED-FOR"]], [[16, 25, 29, 29, "USED-FOR"], [29, 29, 33, 34, "USED-FOR"], [37, 38, 33, 34, "HYPONYM-OF"]], [[48, 49, 46, 46, "USED-FOR"], [48, 49, 52, 53, "USED-FOR"]], [[64, 66, 59, 61, "USED-FOR"], [69, 71, 64, 66, "USED-FOR"], [74, 74, 77, 78, "PART-OF"]], [], [[135, 136, 127, 129, "USED-FOR"]], [[139, 139, 144, 147, "USED-FOR"], [147, 147, 149, 154, "PART-OF"]], [[157, 157, 160, 160, "CONJUNCTION"], [168, 168, 170, 171, "CONJUNCTION"]]] | CVPR_2004_21_abs | train | [{"sentence": "Uncertainty handling plays an important role during shape tracking .", "entities": [{"text": "Uncertainty handling", "type": "Task"}, {"text": "shape tracking", "type": "Task"}], "relations": [{"head": "Uncertainty handling", "tail": "shape tracking", "label": "USED-FOR", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB- .", "entities": [{"text": "fusion of measurement information with system dynamics and shape priors", "type": "Method"}, {"text": "tracking", "type": "Task"}, {"text": "noisy images", "type": "Material"}, {"text": "ultrasound sequences", "type": "Material"}], "relations": [{"head": "fusion of measurement information with system dynamics and shape priors", "tail": "tracking", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "tracking", "tail": "noisy images", "label": "USED-FOR", "head_type": "Task", "tail_type": "Material"}, {"head": "ultrasound sequences", "tail": "noisy images", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Material"}]}, {"sentence": "Nevertheless , this approach required user initialization of the tracking process .", "entities": [{"text": "approach", "type": "Generic"}, {"text": "user initialization", "type": "OtherScientificTerm"}, {"text": "tracking process", "type": "Method"}], "relations": [{"head": "user initialization", "tail": "approach", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Generic"}, {"head": "user initialization", "tail": "tracking process", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "This paper solves the automatic initial-ization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework .", "entities": [{"text": "automatic initial-ization problem", "type": "Task"}, {"text": "boosted shape detection", "type": "Method"}, {"text": "generic measurement process", "type": "Method"}, {"text": "it", "type": "Generic"}, {"text": "tracking framework", "type": "Method"}], "relations": [{"head": "boosted shape detection", "tail": "automatic initial-ization problem", "label": "USED-FOR", "head_type": "Method", "tail_type": "Task"}, {"head": "generic measurement process", "tail": "boosted shape detection", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}, {"head": "it", "tail": "tracking framework", "label": "PART-OF", "head_type": "Generic", "tail_type": "Method"}]}, {"sentence": "We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment , fusion with the predicted shape prior , and fusion with subspace constraints .", "entities": [{"text": "local detection uncertainties", "type": "OtherScientificTerm"}, {"text": "local detection uncertainties of multiple shape candidates", "type": "OtherScientificTerm"}, {"text": "shape alignment", "type": "OtherScientificTerm"}, {"text": "predicted shape prior", "type": "OtherScientificTerm"}, {"text": "subspace constraints", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "As a result , we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood .", "entities": [{"text": "posterior shape model", "type": "Method"}, {"text": "maximum likelihood", "type": "OtherScientificTerm"}], "relations": [{"head": "maximum likelihood", "tail": "posterior shape model", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart .", "entities": [{"text": "framework", "type": "Generic"}, {"text": "automatic tracking of endocardium", "type": "Task"}, {"text": "endocardium", "type": "OtherScientificTerm"}, {"text": "ultrasound sequences of the human heart", "type": "Material"}], "relations": [{"head": "framework", "tail": "automatic tracking of endocardium", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "endocardium", "tail": "ultrasound sequences of the human heart", "label": "PART-OF", "head_type": "OtherScientificTerm", "tail_type": "Material"}]}, {"sentence": "Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations .", "entities": [{"text": "detection", "type": "Task"}, {"text": "tracking", "type": "Task"}, {"text": "approaches", "type": "Generic"}, {"text": "inter-expert variations", "type": "OtherScientificTerm"}], "relations": [{"head": "detection", "tail": "tracking", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}, {"head": "approaches", "tail": "inter-expert variations", "label": "CONJUNCTION", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}] |
[[[12, 13], [15, 15], [37, 37], [47, 47]]] | [["We", "present", "a", "syntax-based", "constraint", "for", "word", "alignment", ",", "known", "as", "the", "cohesion", "constraint", "."], ["It", "requires", "disjoint", "English", "phrases", "to", "be", "mapped", "to", "non-overlapping", "intervals", "in", "the", "French", "sentence", "."], ["We", "evaluate", "the", "utility", "of", "this", "constraint", "in", "two", "different", "algorithms", "."], ["The", "results", "show", "that", "it", "can", "provide", "a", "significant", "improvement", "in", "alignment", "quality", "."]] | [[[3, 4, "OtherScientificTerm"], [6, 7, "Task"], [12, 13, "OtherScientificTerm"]], [[15, 15, "Generic"], [18, 19, "Material"], [28, 29, "Material"]], [[37, 37, "Generic"], [41, 41, "Generic"]], [[47, 47, "Generic"], [54, 55, "Metric"]]] | [[[3, 4, 6, 7, "USED-FOR"], [12, 13, 3, 4, "HYPONYM-OF"]], [[18, 19, 15, 15, "USED-FOR"]], [[41, 41, 37, 37, "EVALUATE-FOR"]], [[54, 55, 47, 47, "EVALUATE-FOR"]]] | N03-2017 | train | [{"sentence": "We present a syntax-based constraint for word alignment , known as the cohesion constraint .", "entities": [{"text": "syntax-based constraint", "type": "OtherScientificTerm"}, {"text": "word alignment", "type": "Task"}, {"text": "cohesion constraint", "type": "OtherScientificTerm"}], "relations": [{"head": "syntax-based constraint", "tail": "word alignment", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}, {"head": "cohesion constraint", "tail": "syntax-based constraint", "label": "HYPONYM-OF", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence .", "entities": [{"text": "It", "type": "Generic"}, {"text": "English phrases", "type": "Material"}, {"text": "French sentence", "type": "Material"}], "relations": [{"head": "English phrases", "tail": "It", "label": "USED-FOR", "head_type": "Material", "tail_type": "Generic"}]}, {"sentence": "We evaluate the utility of this constraint in two different algorithms .", "entities": [{"text": "constraint", "type": "Generic"}, {"text": "algorithms", "type": "Generic"}], "relations": [{"head": "algorithms", "tail": "constraint", "label": "EVALUATE-FOR", "head_type": "Generic", "tail_type": "Generic"}]}, {"sentence": "The results show that it can provide a significant improvement in alignment quality .", "entities": [{"text": "it", "type": "Generic"}, {"text": "alignment quality", "type": "Metric"}], "relations": [{"head": "alignment quality", "tail": "it", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Generic"}]}] |
[[[16, 19], [49, 50]], [[6, 10], [37, 38]]] | [["This", "paper", "considers", "the", "problem", "of", "automatic", "assessment", "of", "local", "coherence", "."], ["We", "present", "a", "novel", "entity-based", "representation", "of", "discourse", "which", "is", "inspired", "by", "Centering", "Theory", "and", "can", "be", "computed", "automatically", "from", "raw", "text", "."], ["We", "view", "coherence", "assessment", "as", "a", "ranking", "learning", "problem", "and", "show", "that", "the", "proposed", "discourse", "representation", "supports", "the", "effective", "learning", "of", "a", "ranking", "function", "."], ["Our", "experiments", "demonstrate", "that", "the", "induced", "model", "achieves", "significantly", "higher", "accuracy", "than", "a", "state-of-the-art", "coherence", "model", "."]] | [[[6, 10, "Task"]], [[16, 19, "Method"], [24, 25, "Method"], [32, 33, "Material"]], [[37, 38, "Task"], [41, 43, "Task"], [49, 50, "Method"], [57, 58, "OtherScientificTerm"]], [[65, 66, "Method"], [70, 70, "Metric"], [74, 75, "Method"]]] | [[], [[24, 25, 16, 19, "USED-FOR"], [32, 33, 16, 19, "USED-FOR"]], [[41, 43, 37, 38, "USED-FOR"], [49, 50, 57, 58, "USED-FOR"]], [[65, 66, 74, 75, "COMPARE"], [70, 70, 65, 66, "EVALUATE-FOR"], [70, 70, 74, 75, "EVALUATE-FOR"]]] | P05-1018 | train | [{"sentence": "This paper considers the problem of automatic assessment of local coherence .", "entities": [{"text": "automatic assessment of local coherence", "type": "Task"}], "relations": []}, {"sentence": "We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text .", "entities": [{"text": "entity-based representation of discourse", "type": "Method"}, {"text": "Centering Theory", "type": "Method"}, {"text": "raw text", "type": "Material"}], "relations": [{"head": "Centering Theory", "tail": "entity-based representation of discourse", "label": "USED-FOR", "head_type": "Method", "tail_type": "Method"}, {"head": "raw text", "tail": "entity-based representation of discourse", "label": "USED-FOR", "head_type": "Material", "tail_type": "Method"}]}, {"sentence": "We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function .", "entities": [{"text": "coherence assessment", "type": "Task"}, {"text": "ranking learning problem", "type": "Task"}, {"text": "discourse representation", "type": "Method"}, {"text": "ranking function", "type": "OtherScientificTerm"}], "relations": [{"head": "ranking learning problem", "tail": "coherence assessment", "label": "USED-FOR", "head_type": "Task", "tail_type": "Task"}, {"head": "discourse representation", "tail": "ranking function", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model .", "entities": [{"text": "induced model", "type": "Method"}, {"text": "accuracy", "type": "Metric"}, {"text": "coherence model", "type": "Method"}], "relations": [{"head": "induced model", "tail": "coherence model", "label": "COMPARE", "head_type": "Method", "tail_type": "Method"}, {"head": "accuracy", "tail": "induced model", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Method"}, {"head": "accuracy", "tail": "coherence model", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Method"}]}] |
[[[4, 6], [19, 19], [88, 88], [130, 130]], [[21, 21], [40, 40]]] | [["This", "paper", "introduces", "a", "robust", "interactive", "method", "for", "speech", "understanding", "."], ["The", "generalized", "LR", "parsing", "is", "enhanced", "in", "this", "approach", "."], ["Parsing", "proceeds", "from", "left", "to", "right", "correcting", "minor", "errors", "."], ["When", "a", "very", "noisy", "portion", "is", "detected", ",", "the", "parser", "skips", "that", "portion", "using", "a", "fake", "non-terminal", "symbol", "."], ["The", "unidentified", "portion", "is", "resolved", "by", "re-utterance", "of", "that", "portion", "which", "is", "parsed", "very", "efficiently", "by", "using", "the", "parse", "record", "of", "the", "first", "utterance", "."], ["The", "user", "does", "not", "have", "to", "speak", "the", "whole", "sentence", "again", "."], ["This", "method", "is", "also", "capable", "of", "handling", "unknown", "words", ",", "which", "is", "important", "in", "practical", "systems", "."], ["Detected", "unknown", "words", "can", "be", "incrementally", "incorporated", "into", "the", "dictionary", "after", "the", "interaction", "with", "the", "user", "."], ["A", "pilot", "system", "has", "shown", "great", "effectiveness", "of", "this", "approach", "."]] | [[[4, 6, "Generic"], [8, 9, "Task"]], [[12, 14, "Method"], [19, 19, "Generic"]], [[21, 21, "Method"]], [[40, 40, "Method"], [47, 48, "OtherScientificTerm"]], [], [], [[88, 88, "Generic"], [94, 95, "OtherScientificTerm"]], [], [[130, 130, "Generic"]]] | [[[4, 6, 8, 9, "USED-FOR"]], [[19, 19, 12, 14, "USED-FOR"]], [], [[47, 48, 40, 40, "USED-FOR"]], [], [], [[88, 88, 94, 95, "USED-FOR"]], [], []] | C92-3165 | train | [{"sentence": "This paper introduces a robust interactive method for speech understanding .", "entities": [{"text": "robust interactive method", "type": "Generic"}, {"text": "speech understanding", "type": "Task"}], "relations": [{"head": "robust interactive method", "tail": "speech understanding", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}, {"sentence": "The generalized LR parsing is enhanced in this approach .", "entities": [{"text": "generalized LR parsing", "type": "Method"}, {"text": "approach", "type": "Generic"}], "relations": [{"head": "approach", "tail": "generalized LR parsing", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Method"}]}, {"sentence": "Parsing proceeds from left to right correcting minor errors .", "entities": [{"text": "Parsing", "type": "Method"}], "relations": []}, {"sentence": "When a very noisy portion is detected , the parser skips that portion using a fake non-terminal symbol .", "entities": [{"text": "parser", "type": "Method"}, {"text": "non-terminal symbol", "type": "OtherScientificTerm"}], "relations": [{"head": "non-terminal symbol", "tail": "parser", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "The unidentified portion is resolved by re-utterance of that portion which is parsed very efficiently by using the parse record of the first utterance .", "entities": [], "relations": []}, {"sentence": "The user does not have to speak the whole sentence again .", "entities": [], "relations": []}, {"sentence": "This method is also capable of handling unknown words , which is important in practical systems .", "entities": [{"text": "method", "type": "Generic"}, {"text": "unknown words", "type": "OtherScientificTerm"}], "relations": [{"head": "method", "tail": "unknown words", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Detected unknown words can be incrementally incorporated into the dictionary after the interaction with the user .", "entities": [], "relations": []}, {"sentence": "A pilot system has shown great effectiveness of this approach .", "entities": [{"text": "approach", "type": "Generic"}], "relations": []}] |
[] | [["This", "paper", "shows", "that", "it", "is", "very", "often", "possible", "to", "identify", "the", "source", "language", "of", "medium-length", "speeches", "in", "the", "EUROPARL", "corpus", "on", "the", "basis", "of", "frequency", "counts", "of", "word", "n-grams", "-LRB-", "87.2", "%", "-96.7", "%", "accuracy", "depending", "on", "classification", "method", "-RRB-", "."], ["The", "paper", "also", "examines", "in", "detail", "which", "positive", "markers", "are", "most", "powerful", "and", "identifies", "a", "number", "of", "linguistic", "aspects", "as", "well", "as", "culture", "-", "and", "domain-related", "ones", "."]] | [[[15, 16, "Material"], [19, 20, "Material"], [25, 29, "Method"], [28, 29, "OtherScientificTerm"], [35, 35, "Metric"], [38, 39, "Method"]], [[49, 50, "OtherScientificTerm"]]] | [[[15, 16, 19, 20, "PART-OF"], [35, 35, 38, 39, "EVALUATE-FOR"]], []] | C08-1118 | train | [{"sentence": "This paper shows that it is very often possible to identify the source language of medium-length speeches in the EUROPARL corpus on the basis of frequency counts of word n-grams -LRB- 87.2 % -96.7 % accuracy depending on classification method -RRB- .", "entities": [{"text": "medium-length speeches", "type": "Material"}, {"text": "EUROPARL corpus", "type": "Material"}, {"text": "frequency counts of word n-grams", "type": "Method"}, {"text": "word n-grams", "type": "OtherScientificTerm"}, {"text": "accuracy", "type": "Metric"}, {"text": "classification method", "type": "Method"}], "relations": [{"head": "medium-length speeches", "tail": "EUROPARL corpus", "label": "PART-OF", "head_type": "Material", "tail_type": "Material"}, {"head": "accuracy", "tail": "classification method", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Method"}]}, {"sentence": "The paper also examines in detail which positive markers are most powerful and identifies a number of linguistic aspects as well as culture - and domain-related ones .", "entities": [{"text": "positive markers", "type": "OtherScientificTerm"}], "relations": []}] |
[[[3, 8], [71, 71]], [[60, 60], [84, 84]], [[24, 25], [55, 56]], [[11, 14], [74, 74]]] | [["We", "investigated", "whether", "automatic", "phonetic", "transcriptions", "-LRB-", "APTs", "-RRB-", "can", "replace", "manually", "verified", "phonetic", "transcriptions", "-LRB-", "MPTs", "-RRB-", "in", "a", "large", "corpus-based", "study", "on", "pronunciation", "variation", "."], ["To", "this", "end", ",", "we", "compared", "the", "performance", "of", "both", "transcription", "types", "in", "a", "classification", "experiment", "aimed", "at", "establishing", "the", "direct", "influence", "of", "a", "particular", "situational", "setting", "on", "pronunciation", "variation", "."], ["We", "trained", "classifiers", "on", "the", "speech", "processes", "extracted", "from", "the", "alignments", "of", "an", "APT", "and", "an", "MPT", "with", "a", "canonical", "transcription", "."], ["We", "tested", "whether", "the", "classifiers", "were", "equally", "good", "at", "verifying", "whether", "unknown", "transcriptions", "represent", "read", "speech", "or", "telephone", "dialogues", ",", "and", "whether", "the", "same", "speech", "processes", "were", "identified", "to", "distinguish", "between", "transcriptions", "of", "the", "two", "situational", "settings", "."], ["Our", "results", "not", "only", "show", "that", "similar", "distinguishing", "speech", "processes", "were", "identified", ";", "our", "APT-based", "classifier", "yielded", "better", "classification", "accuracy", "than", "the", "MPT-based", "classifier", "whilst", "using", "fewer", "classification", "features", "."]] | [[[3, 8, "OtherScientificTerm"], [11, 14, "OtherScientificTerm"], [24, 25, "Task"]], [[41, 41, "Task"], [55, 56, "Task"]], [[60, 60, "Method"], [63, 64, "Material"], [68, 68, "OtherScientificTerm"], [71, 71, "OtherScientificTerm"], [74, 74, "OtherScientificTerm"], [77, 78, "OtherScientificTerm"]], [[84, 84, "Material"], [91, 92, "OtherScientificTerm"], [94, 95, "Material"], [97, 98, "Material"], [104, 105, "Material"]], [[132, 133, "Method"], [136, 137, "Metric"], [140, 141, "Method"], [145, 146, "OtherScientificTerm"]]] | [[[3, 8, 11, 14, "COMPARE"], [3, 8, 24, 25, "USED-FOR"], [11, 14, 24, 25, "USED-FOR"]], [], [[63, 64, 60, 60, "USED-FOR"], [68, 68, 63, 64, "USED-FOR"], [68, 68, 71, 71, "USED-FOR"], [68, 68, 74, 74, "USED-FOR"], [71, 71, 74, 74, "CONJUNCTION"], [77, 78, 68, 68, "USED-FOR"]], [[84, 84, 91, 92, "USED-FOR"], [91, 92, 94, 95, "USED-FOR"], [91, 92, 97, 98, "USED-FOR"], [94, 95, 97, 98, "CONJUNCTION"]], [[132, 133, 140, 141, "COMPARE"], [136, 137, 132, 133, "EVALUATE-FOR"], [136, 137, 140, 141, "EVALUATE-FOR"], [145, 146, 132, 133, "USED-FOR"], [145, 146, 140, 141, "USED-FOR"]]] | INTERSPEECH_2006_28_abs | train | [{"sentence": "We investigated whether automatic phonetic transcriptions -LRB- APTs -RRB- can replace manually verified phonetic transcriptions -LRB- MPTs -RRB- in a large corpus-based study on pronunciation variation .", "entities": [{"text": "automatic phonetic transcriptions -LRB- APTs -RRB-", "type": "OtherScientificTerm"}, {"text": "manually verified phonetic transcriptions", "type": "OtherScientificTerm"}, {"text": "pronunciation variation", "type": "Task"}], "relations": [{"head": "automatic phonetic transcriptions -LRB- APTs -RRB-", "tail": "manually verified phonetic transcriptions", "label": "COMPARE", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "automatic phonetic transcriptions -LRB- APTs -RRB-", "tail": "pronunciation variation", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}, {"head": "manually verified phonetic transcriptions", "tail": "pronunciation variation", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Task"}]}, {"sentence": "To this end , we compared the performance of both transcription types in a classification experiment aimed at establishing the direct influence of a particular situational setting on pronunciation variation .", "entities": [{"text": "classification", "type": "Task"}, {"text": "pronunciation variation", "type": "Task"}], "relations": []}, {"sentence": "We trained classifiers on the speech processes extracted from the alignments of an APT and an MPT with a canonical transcription .", "entities": [{"text": "classifiers", "type": "Method"}, {"text": "speech processes", "type": "Material"}, {"text": "alignments", "type": "OtherScientificTerm"}, {"text": "APT", "type": "OtherScientificTerm"}, {"text": "MPT", "type": "OtherScientificTerm"}, {"text": "canonical transcription", "type": "OtherScientificTerm"}], "relations": [{"head": "speech processes", "tail": "classifiers", "label": "USED-FOR", "head_type": "Material", "tail_type": "Method"}, {"head": "alignments", "tail": "speech processes", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Material"}, {"head": "alignments", "tail": "APT", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "alignments", "tail": "MPT", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "APT", "tail": "MPT", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}, {"head": "canonical transcription", "tail": "alignments", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "We tested whether the classifiers were equally good at verifying whether unknown transcriptions represent read speech or telephone dialogues , and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings .", "entities": [{"text": "classifiers", "type": "Material"}, {"text": "unknown transcriptions", "type": "OtherScientificTerm"}, {"text": "read speech", "type": "Material"}, {"text": "telephone dialogues", "type": "Material"}, {"text": "speech processes", "type": "Material"}], "relations": [{"head": "classifiers", "tail": "unknown transcriptions", "label": "USED-FOR", "head_type": "Material", "tail_type": "OtherScientificTerm"}, {"head": "unknown transcriptions", "tail": "read speech", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Material"}, {"head": "unknown transcriptions", "tail": "telephone dialogues", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Material"}, {"head": "read speech", "tail": "telephone dialogues", "label": "CONJUNCTION", "head_type": "Material", "tail_type": "Material"}]}, {"sentence": "Our results not only show that similar distinguishing speech processes were identified ; our APT-based classifier yielded better classification accuracy than the MPT-based classifier whilst using fewer classification features .", "entities": [{"text": "APT-based classifier", "type": "Method"}, {"text": "classification accuracy", "type": "Metric"}, {"text": "MPT-based classifier", "type": "Method"}, {"text": "classification features", "type": "OtherScientificTerm"}], "relations": [{"head": "APT-based classifier", "tail": "MPT-based classifier", "label": "COMPARE", "head_type": "Method", "tail_type": "Method"}, {"head": "classification accuracy", "tail": "APT-based classifier", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Method"}, {"head": "classification accuracy", "tail": "MPT-based classifier", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Method"}, {"head": "classification features", "tail": "APT-based classifier", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "classification features", "tail": "MPT-based classifier", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}] |
[[[0, 1], [6, 6], [27, 27]], [[52, 54], [103, 103], [130, 130]]] | [["Machine", "reading", "is", "a", "relatively", "new", "field", "that", "features", "computer", "programs", "designed", "to", "read", "flowing", "text", "and", "extract", "fact", "assertions", "expressed", "by", "the", "narrative", "content", "."], ["This", "task", "involves", "two", "core", "technologies", ":", "natural", "language", "processing", "-LRB-", "NLP", "-RRB-", "and", "information", "extraction", "-LRB-", "IE", "-RRB-", "."], ["In", "this", "paper", "we", "describe", "a", "machine", "reading", "system", "that", "we", "have", "developed", "within", "a", "cognitive", "architecture", "."], ["We", "show", "how", "we", "have", "integrated", "into", "the", "framework", "several", "levels", "of", "knowledge", "for", "a", "particular", "domain", ",", "ideas", "from", "cognitive", "semantics", "and", "construction", "grammar", ",", "plus", "tools", "from", "prior", "NLP", "and", "IE", "research", "."], ["The", "result", "is", "a", "system", "that", "is", "capable", "of", "reading", "and", "interpreting", "complex", "and", "fairly", "idiosyncratic", "texts", "in", "the", "family", "history", "domain", "."], ["We", "describe", "the", "architecture", "and", "performance", "of", "the", "system", "."], ["After", "presenting", "the", "results", "from", "several", "evaluations", "that", "we", "have", "carried", "out", ",", "we", "summarize", "possible", "future", "directions", "."]] | [[[0, 1, "Task"], [6, 6, "Generic"], [9, 10, "Method"], [14, 15, "Material"], [18, 19, "OtherScientificTerm"], [23, 24, "Material"]], [[27, 27, "Generic"], [33, 38, "Method"], [40, 44, "Task"]], [[52, 54, "Task"], [61, 62, "Method"]], [[84, 85, "OtherScientificTerm"], [87, 88, "Method"], [93, 94, "Task"], [96, 97, "Task"]], [[103, 103, "Generic"], [114, 115, "Material"], [118, 120, "Material"]], [[130, 130, "Generic"]], []] | [[[9, 10, 14, 15, "USED-FOR"], [9, 10, 18, 19, "USED-FOR"], [18, 19, 23, 24, "FEATURE-OF"]], [[33, 38, 27, 27, "PART-OF"], [40, 44, 27, 27, "PART-OF"]], [[61, 62, 52, 54, "FEATURE-OF"]], [[84, 85, 87, 88, "CONJUNCTION"], [93, 94, 96, 97, "CONJUNCTION"]], [[103, 103, 114, 115, "USED-FOR"], [118, 120, 114, 115, "FEATURE-OF"]], [], []] | AAAI_2015_10_abs | train | [{"sentence": "Machine reading is a relatively new field that features computer programs designed to read flowing text and extract fact assertions expressed by the narrative content .", "entities": [{"text": "Machine reading", "type": "Task"}, {"text": "field", "type": "Generic"}, {"text": "computer programs", "type": "Method"}, {"text": "flowing text", "type": "Material"}, {"text": "fact assertions", "type": "OtherScientificTerm"}, {"text": "narrative content", "type": "Material"}], "relations": [{"head": "computer programs", "tail": "flowing text", "label": "USED-FOR", "head_type": "Method", "tail_type": "Material"}, {"head": "computer programs", "tail": "fact assertions", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}, {"head": "fact assertions", "tail": "narrative content", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "Material"}]}, {"sentence": "This task involves two core technologies : natural language processing -LRB- NLP -RRB- and information extraction -LRB- IE -RRB- .", "entities": [{"text": "task", "type": "Generic"}, {"text": "natural language processing -LRB- NLP -RRB-", "type": "Method"}, {"text": "information extraction -LRB- IE -RRB-", "type": "Task"}], "relations": [{"head": "natural language processing -LRB- NLP -RRB-", "tail": "task", "label": "PART-OF", "head_type": "Method", "tail_type": "Generic"}, {"head": "information extraction -LRB- IE -RRB-", "tail": "task", "label": "PART-OF", "head_type": "Task", "tail_type": "Generic"}]}, {"sentence": "In this paper we describe a machine reading system that we have developed within a cognitive architecture .", "entities": [{"text": "machine reading system", "type": "Task"}, {"text": "cognitive architecture", "type": "Method"}], "relations": [{"head": "cognitive architecture", "tail": "machine reading system", "label": "FEATURE-OF", "head_type": "Method", "tail_type": "Task"}]}, {"sentence": "We show how we have integrated into the framework several levels of knowledge for a particular domain , ideas from cognitive semantics and construction grammar , plus tools from prior NLP and IE research .", "entities": [{"text": "cognitive semantics", "type": "OtherScientificTerm"}, {"text": "construction grammar", "type": "Method"}, {"text": "prior NLP", "type": "Task"}, {"text": "IE research", "type": "Task"}], "relations": [{"head": "cognitive semantics", "tail": "construction grammar", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "prior NLP", "tail": "IE research", "label": "CONJUNCTION", "head_type": "Task", "tail_type": "Task"}]}, {"sentence": "The result is a system that is capable of reading and interpreting complex and fairly idiosyncratic texts in the family history domain .", "entities": [{"text": "system", "type": "Generic"}, {"text": "idiosyncratic texts", "type": "Material"}, {"text": "family history domain", "type": "Material"}], "relations": [{"head": "system", "tail": "idiosyncratic texts", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Material"}, {"head": "family history domain", "tail": "idiosyncratic texts", "label": "FEATURE-OF", "head_type": "Material", "tail_type": "Material"}]}, {"sentence": "We describe the architecture and performance of the system .", "entities": [{"text": "system", "type": "Generic"}], "relations": []}, {"sentence": "After presenting the results from several evaluations that we have carried out , we summarize possible future directions .", "entities": [], "relations": []}] |
[] | [["Nonstationary", "chaotic", "behavior", "is", "not", "an", "oxymoron", "."], ["We", "present", "two", "methods", "for", "capturing", "nonstationary", "chaos", ",", "then", "present", "a", "few", "examples", "including", "biological", "signals", ",", "ocean", "waves", "and", "traffic", "flow", "."], ["The", "issue", "is", "of", "practical", "interest", "because", "it", "is", "often", "useful", "to", "capture", "when", "nonstationary", "events", "take", "place", "and", "it", "is", "desirable", "to", "know", "over", "what", "periods", "a", "signal", "is", "stationary", "."]] | [[[0, 2, "OtherScientificTerm"], [6, 6, "OtherScientificTerm"]], [[11, 11, "Generic"], [14, 15, "OtherScientificTerm"], [21, 21, "Generic"], [23, 24, "Material"], [26, 27, "Material"], [29, 30, "Material"]], [[46, 47, "OtherScientificTerm"]]] | [[], [[11, 11, 14, 15, "USED-FOR"], [23, 24, 21, 21, "HYPONYM-OF"], [23, 24, 26, 27, "CONJUNCTION"], [26, 27, 21, 21, "HYPONYM-OF"], [26, 27, 29, 30, "CONJUNCTION"], [29, 30, 21, 21, "HYPONYM-OF"]], []] | ICASSP_1997_707_abs | train | [{"sentence": "Nonstationary chaotic behavior is not an oxymoron .", "entities": [{"text": "Nonstationary chaotic behavior", "type": "OtherScientificTerm"}, {"text": "oxymoron", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "We present two methods for capturing nonstationary chaos , then present a few examples including biological signals , ocean waves and traffic flow .", "entities": [{"text": "methods", "type": "Generic"}, {"text": "nonstationary chaos", "type": "OtherScientificTerm"}, {"text": "examples", "type": "Generic"}, {"text": "biological signals", "type": "Material"}, {"text": "ocean waves", "type": "Material"}, {"text": "traffic flow", "type": "Material"}], "relations": [{"head": "methods", "tail": "nonstationary chaos", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "biological signals", "tail": "examples", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Generic"}, {"head": "biological signals", "tail": "ocean waves", "label": "CONJUNCTION", "head_type": "Material", "tail_type": "Material"}, {"head": "ocean waves", "tail": "examples", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Generic"}, {"head": "ocean waves", "tail": "traffic flow", "label": "CONJUNCTION", "head_type": "Material", "tail_type": "Material"}, {"head": "traffic flow", "tail": "examples", "label": "HYPONYM-OF", "head_type": "Material", "tail_type": "Generic"}]}, {"sentence": "The issue is of practical interest because it is often useful to capture when nonstationary events take place and it is desirable to know over what periods a signal is stationary .", "entities": [{"text": "nonstationary events", "type": "OtherScientificTerm"}], "relations": []}] |
[[[4, 5], [75, 76], [92, 92]], [[13, 14], [32, 32], [54, 54]], [[45, 48], [63, 64], [82, 83]]] | [["This", "paper", "presents", "a", "formal", "analysis", "for", "a", "large", "class", "of", "words", "called", "alternative", "markers", ",", "which", "includes", "other", "-LRB-", "than", "-RRB-", ",", "such", "-LRB-", "as", "-RRB-", ",", "and", "besides", "."], ["These", "words", "appear", "frequently", "enough", "in", "dialog", "to", "warrant", "serious", "attention", ",", "yet", "present", "natural", "language", "search", "engines", "perform", "poorly", "on", "queries", "containing", "them", "."], ["I", "show", "that", "the", "performance", "of", "a", "search", "engine", "can", "be", "improved", "dramatically", "by", "incorporating", "an", "approximation", "of", "the", "formal", "analysis", "that", "is", "compatible", "with", "the", "search", "engine", "'s", "operational", "semantics", "."], ["The", "value", "of", "this", "approach", "is", "that", "as", "the", "operational", "semantics", "of", "natural", "language", "applications", "improve", ",", "even", "larger", "improvements", "are", "possible", "."]] | [[[4, 5, "Method"], [13, 14, "OtherScientificTerm"]], [[32, 32, "Generic"], [37, 37, "OtherScientificTerm"], [45, 48, "Method"], [54, 54, "Generic"]], [[63, 64, "Method"], [72, 76, "Method"], [75, 76, "Method"], [82, 83, "Method"], [85, 86, "OtherScientificTerm"]], [[92, 92, "Generic"], [97, 98, "OtherScientificTerm"], [100, 102, "Task"]]] | [[[4, 5, 13, 14, "USED-FOR"]], [[32, 32, 37, 37, "PART-OF"]], [[72, 76, 63, 64, "PART-OF"], [85, 86, 82, 83, "PART-OF"]], [[97, 98, 100, 102, "PART-OF"]]] | P01-1009 | train | [{"sentence": "This paper presents a formal analysis for a large class of words called alternative markers , which includes other -LRB- than -RRB- , such -LRB- as -RRB- , and besides .", "entities": [{"text": "formal analysis", "type": "Method"}, {"text": "alternative markers", "type": "OtherScientificTerm"}], "relations": [{"head": "formal analysis", "tail": "alternative markers", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "These words appear frequently enough in dialog to warrant serious attention , yet present natural language search engines perform poorly on queries containing them .", "entities": [{"text": "words", "type": "Generic"}, {"text": "dialog", "type": "OtherScientificTerm"}, {"text": "natural language search engines", "type": "Method"}, {"text": "them", "type": "Generic"}], "relations": [{"head": "words", "tail": "dialog", "label": "PART-OF", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}, {"sentence": "I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine 's operational semantics .", "entities": [{"text": "search engine", "type": "Method"}, {"text": "approximation of the formal analysis", "type": "Method"}, {"text": "formal analysis", "type": "Method"}, {"text": "search engine", "type": "Method"}, {"text": "operational semantics", "type": "OtherScientificTerm"}], "relations": [{"head": "approximation of the formal analysis", "tail": "search engine", "label": "PART-OF", "head_type": "Method", "tail_type": "Method"}, {"head": "operational semantics", "tail": "search engine", "label": "PART-OF", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "The value of this approach is that as the operational semantics of natural language applications improve , even larger improvements are possible .", "entities": [{"text": "approach", "type": "Generic"}, {"text": "operational semantics", "type": "OtherScientificTerm"}, {"text": "natural language applications", "type": "Task"}], "relations": [{"head": "operational semantics", "tail": "natural language applications", "label": "PART-OF", "head_type": "OtherScientificTerm", "tail_type": "Task"}]}] |
[[[95, 96], [115, 118]], [[128, 128], [146, 146]], [[150, 150], [162, 162]]] | [["In", "this", "paper", ",", "we", "address", "the", "problem", "of", "combining", "several", "language", "models", "-LRB-", "LMs", "-RRB-", "."], ["We", "find", "that", "simple", "interpolation", "methods", ",", "like", "log-linear", "and", "linear", "interpolation", ",", "improve", "the", "performance", "but", "fall", "short", "of", "the", "performance", "of", "an", "oracle", "."], ["The", "oracle", "knows", "the", "reference", "word", "string", "and", "selects", "the", "word", "string", "with", "the", "best", "performance", "-LRB-", "typically", ",", "word", "or", "semantic", "error", "rate", "-RRB-", "from", "a", "list", "of", "word", "strings", ",", "where", "each", "word", "string", "has", "been", "obtained", "by", "using", "a", "different", "LM", "."], ["Actually", ",", "the", "oracle", "acts", "like", "a", "dynamic", "combiner", "with", "hard", "decisions", "using", "the", "reference", "."], ["We", "provide", "experimental", "results", "that", "clearly", "show", "the", "need", "for", "a", "dynamic", "language", "model", "combination", "to", "improve", "the", "performance", "further", "."], ["We", "suggest", "a", "method", "that", "mimics", "the", "behavior", "of", "the", "oracle", "using", "a", "neural", "network", "or", "a", "decision", "tree", "."], ["The", "method", "amounts", "to", "tagging", "LMs", "with", "confidence", "measures", "and", "picking", "the", "best", "hypothesis", "corresponding", "to", "the", "LM", "with", "the", "best", "confidence", "."]] | [[[11, 15, "Method"]], [[21, 22, "Method"], [25, 28, "Method"]], [[62, 66, "Metric"], [86, 86, "Method"]], [[95, 96, "Method"], [98, 99, "OtherScientificTerm"]], [[115, 118, "Method"]], [[128, 128, "Generic"], [138, 139, "Method"], [142, 143, "Method"]], [[146, 146, "Generic"], [150, 150, "Method"], [152, 153, "Metric"], [162, 162, "Method"]]] | [[], [[25, 28, 21, 22, "HYPONYM-OF"]], [], [[98, 99, 95, 96, "FEATURE-OF"]], [], [[138, 139, 128, 128, "USED-FOR"], [142, 143, 128, 128, "USED-FOR"], [142, 143, 138, 139, "CONJUNCTION"]], [[146, 146, 150, 150, "USED-FOR"], [152, 153, 146, 146, "USED-FOR"]]] | H01-1058 | train | [{"sentence": "In this paper , we address the problem of combining several language models -LRB- LMs -RRB- .", "entities": [{"text": "language models -LRB- LMs -RRB-", "type": "Method"}], "relations": []}, {"sentence": "We find that simple interpolation methods , like log-linear and linear interpolation , improve the performance but fall short of the performance of an oracle .", "entities": [{"text": "interpolation methods", "type": "Method"}, {"text": "log-linear and linear interpolation", "type": "Method"}], "relations": [{"head": "log-linear and linear interpolation", "tail": "interpolation methods", "label": "HYPONYM-OF", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "The oracle knows the reference word string and selects the word string with the best performance -LRB- typically , word or semantic error rate -RRB- from a list of word strings , where each word string has been obtained by using a different LM .", "entities": [{"text": "word or semantic error rate", "type": "Metric"}, {"text": "LM", "type": "Method"}], "relations": []}, {"sentence": "Actually , the oracle acts like a dynamic combiner with hard decisions using the reference .", "entities": [{"text": "dynamic combiner", "type": "Method"}, {"text": "hard decisions", "type": "OtherScientificTerm"}], "relations": [{"head": "hard decisions", "tail": "dynamic combiner", "label": "FEATURE-OF", "head_type": "OtherScientificTerm", "tail_type": "Method"}]}, {"sentence": "We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further .", "entities": [{"text": "dynamic language model combination", "type": "Method"}], "relations": []}, {"sentence": "We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree .", "entities": [{"text": "method", "type": "Generic"}, {"text": "neural network", "type": "Method"}, {"text": "decision tree", "type": "Method"}], "relations": [{"head": "neural network", "tail": "method", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}, {"head": "decision tree", "tail": "method", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}, {"head": "decision tree", "tail": "neural network", "label": "CONJUNCTION", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence .", "entities": [{"text": "method", "type": "Generic"}, {"text": "LMs", "type": "Method"}, {"text": "confidence measures", "type": "Metric"}, {"text": "LM", "type": "Method"}], "relations": [{"head": "method", "tail": "LMs", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Method"}, {"head": "confidence measures", "tail": "method", "label": "USED-FOR", "head_type": "Metric", "tail_type": "Generic"}]}] |
[[[4, 4], [20, 22], [41, 41], [60, 60], [92, 92], [98, 98], [134, 134]]] | [["We", "describe", "a", "new", "method", "for", "the", "representation", "of", "NLP", "structures", "within", "reranking", "approaches", "."], ["We", "make", "use", "of", "a", "conditional", "log-linear", "model", ",", "with", "hidden", "variables", "representing", "the", "assignment", "of", "lexical", "items", "to", "word", "clusters", "or", "word", "senses", "."], ["The", "model", "learns", "to", "automatically", "make", "these", "assignments", "based", "on", "a", "discriminative", "training", "criterion", "."], ["Training", "and", "decoding", "with", "the", "model", "requires", "summing", "over", "an", "exponential", "number", "of", "hidden-variable", "assignments", ":", "the", "required", "summations", "can", "be", "computed", "efficiently", "and", "exactly", "using", "dynamic", "programming", "."], ["As", "a", "case", "study", ",", "we", "apply", "the", "model", "to", "parse", "reranking", "."], ["The", "model", "gives", "an", "F-measure", "improvement", "of", "~", "1.25", "%", "beyond", "the", "base", "parser", ",", "and", "an", "~", "0.25", "%", "improvement", "beyond", "Collins", "-LRB-", "2000", "-RRB-", "reranker", "."], ["Although", "our", "experiments", "are", "focused", "on", "parsing", ",", "the", "techniques", "described", "generalize", "naturally", "to", "NLP", "structures", "other", "than", "parse", "trees", "."]] | [[[4, 4, "Generic"], [9, 10, "OtherScientificTerm"], [12, 13, "Method"]], [[20, 22, "Method"], [25, 26, "OtherScientificTerm"], [34, 35, "OtherScientificTerm"], [37, 38, "OtherScientificTerm"]], [[41, 41, "Generic"], [51, 53, "Metric"]], [[60, 60, "Generic"], [68, 69, "OtherScientificTerm"], [73, 73, "Generic"], [81, 82, "Method"]], [[92, 92, "Generic"], [94, 95, "Task"]], [[98, 98, "Generic"], [101, 101, "Metric"], [109, 110, "Method"], [119, 123, "Method"]], [[131, 131, "Task"], [134, 134, "Generic"], [139, 140, "OtherScientificTerm"], [143, 144, "OtherScientificTerm"]]] | [[[4, 4, 9, 10, "USED-FOR"], [12, 13, 9, 10, "FEATURE-OF"]], [[25, 26, 20, 22, "USED-FOR"], [34, 35, 37, 38, "CONJUNCTION"]], [[51, 53, 41, 41, "USED-FOR"]], [[81, 82, 73, 73, "USED-FOR"]], [[92, 92, 94, 95, "USED-FOR"]], [[98, 98, 109, 110, "COMPARE"], [101, 101, 98, 98, "EVALUATE-FOR"], [109, 110, 119, 123, "COMPARE"]], [[134, 134, 131, 131, "USED-FOR"], [134, 134, 139, 140, "USED-FOR"], [134, 134, 143, 144, "USED-FOR"], [143, 144, 139, 140, "CONJUNCTION"]]] | H05-1064 | train | [{"sentence": "We describe a new method for the representation of NLP structures within reranking approaches .", "entities": [{"text": "method", "type": "Generic"}, {"text": "NLP structures", "type": "OtherScientificTerm"}, {"text": "reranking approaches", "type": "Method"}], "relations": [{"head": "method", "tail": "NLP structures", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "reranking approaches", "tail": "NLP structures", "label": "FEATURE-OF", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "We make use of a conditional log-linear model , with hidden variables representing the assignment of lexical items to word clusters or word senses .", "entities": [{"text": "conditional log-linear model", "type": "Method"}, {"text": "hidden variables", "type": "OtherScientificTerm"}, {"text": "word clusters", "type": "OtherScientificTerm"}, {"text": "word senses", "type": "OtherScientificTerm"}], "relations": [{"head": "hidden variables", "tail": "conditional log-linear model", "label": "USED-FOR", "head_type": "OtherScientificTerm", "tail_type": "Method"}, {"head": "word clusters", "tail": "word senses", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}, {"sentence": "The model learns to automatically make these assignments based on a discriminative training criterion .", "entities": [{"text": "model", "type": "Generic"}, {"text": "discriminative training criterion", "type": "Metric"}], "relations": [{"head": "discriminative training criterion", "tail": "model", "label": "USED-FOR", "head_type": "Metric", "tail_type": "Generic"}]}, {"sentence": "Training and decoding with the model requires summing over an exponential number of hidden-variable assignments : the required summations can be computed efficiently and exactly using dynamic programming .", "entities": [{"text": "model", "type": "Generic"}, {"text": "hidden-variable assignments", "type": "OtherScientificTerm"}, {"text": "summations", "type": "Generic"}, {"text": "dynamic programming", "type": "Method"}], "relations": [{"head": "dynamic programming", "tail": "summations", "label": "USED-FOR", "head_type": "Method", "tail_type": "Generic"}]}, {"sentence": "As a case study , we apply the model to parse reranking .", "entities": [{"text": "model", "type": "Generic"}, {"text": "parse reranking", "type": "Task"}], "relations": [{"head": "model", "tail": "parse reranking", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}, {"sentence": "The model gives an F-measure improvement of ~ 1.25 % beyond the base parser , and an ~ 0.25 % improvement beyond Collins -LRB- 2000 -RRB- reranker .", "entities": [{"text": "model", "type": "Generic"}, {"text": "F-measure", "type": "Metric"}, {"text": "base parser", "type": "Method"}, {"text": "Collins -LRB- 2000 -RRB- reranker", "type": "Method"}], "relations": [{"head": "model", "tail": "base parser", "label": "COMPARE", "head_type": "Generic", "tail_type": "Method"}, {"head": "F-measure", "tail": "model", "label": "EVALUATE-FOR", "head_type": "Metric", "tail_type": "Generic"}, {"head": "base parser", "tail": "Collins -LRB- 2000 -RRB- reranker", "label": "COMPARE", "head_type": "Method", "tail_type": "Method"}]}, {"sentence": "Although our experiments are focused on parsing , the techniques described generalize naturally to NLP structures other than parse trees .", "entities": [{"text": "parsing", "type": "Task"}, {"text": "techniques", "type": "Generic"}, {"text": "NLP structures", "type": "OtherScientificTerm"}, {"text": "parse trees", "type": "OtherScientificTerm"}], "relations": [{"head": "techniques", "tail": "parsing", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}, {"head": "techniques", "tail": "NLP structures", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "techniques", "tail": "parse trees", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "parse trees", "tail": "NLP structures", "label": "CONJUNCTION", "head_type": "OtherScientificTerm", "tail_type": "OtherScientificTerm"}]}] |
[[[4, 4], [76, 76], [106, 106], [117, 117]], [[67, 68], [93, 93]]] | [["This", "paper", "presents", "an", "algorithm", "for", "learning", "the", "time-varying", "shape", "of", "a", "non-rigid", "3D", "object", "from", "uncalibrated", "2D", "tracking", "data", "."], ["We", "model", "shape", "motion", "as", "a", "rigid", "component", "-LRB-", "rotation", "and", "translation", "-RRB-", "combined", "with", "a", "non-rigid", "deformation", "."], ["Reconstruction", "is", "ill-posed", "if", "arbitrary", "deformations", "are", "allowed", "."], ["We", "constrain", "the", "problem", "by", "assuming", "that", "the", "object", "shape", "at", "each", "time", "instant", "is", "drawn", "from", "a", "Gaussian", "distribution", "."], ["Based", "on", "this", "assumption", ",", "the", "algorithm", "simultaneously", "estimates", "3D", "shape", "and", "motion", "for", "each", "time", "frame", ",", "learns", "the", "parameters", "of", "the", "Gaussian", ",", "and", "robustly", "fills-in", "missing", "data", "points", "."], ["We", "then", "extend", "the", "algorithm", "to", "model", "temporal", "smoothness", "in", "object", "shape", ",", "thus", "allowing", "it", "to", "handle", "severe", "cases", "of", "missing", "data", "."]] | [[[4, 4, "Generic"], [6, 14, "Task"], [16, 19, "Material"]], [[23, 24, "OtherScientificTerm"], [27, 28, "Method"], [30, 30, "OtherScientificTerm"], [32, 32, "OtherScientificTerm"], [37, 38, "OtherScientificTerm"]], [[40, 40, "OtherScientificTerm"], [44, 45, "OtherScientificTerm"]], [[57, 58, "OtherScientificTerm"], [67, 68, "Method"]], [[76, 76, "Generic"], [79, 82, "OtherScientificTerm"], [93, 93, "Method"]], [[106, 106, "Generic"], [109, 113, "OtherScientificTerm"], [117, 117, "Generic"], [123, 124, "Material"]]] | [[[4, 4, 6, 14, "USED-FOR"]], [], [], [[67, 68, 57, 58, "USED-FOR"]], [[76, 76, 79, 82, "USED-FOR"]], [[106, 106, 109, 113, "USED-FOR"], [117, 117, 123, 124, "USED-FOR"]]] | NIPS_2003_18_abs | train | [{"sentence": "This paper presents an algorithm for learning the time-varying shape of a non-rigid 3D object from uncalibrated 2D tracking data .", "entities": [{"text": "algorithm", "type": "Generic"}, {"text": "learning the time-varying shape of a non-rigid 3D object", "type": "Task"}, {"text": "uncalibrated 2D tracking data", "type": "Material"}], "relations": [{"head": "algorithm", "tail": "learning the time-varying shape of a non-rigid 3D object", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Task"}]}, {"sentence": "We model shape motion as a rigid component -LRB- rotation and translation -RRB- combined with a non-rigid deformation .", "entities": [{"text": "shape motion", "type": "OtherScientificTerm"}, {"text": "rigid component", "type": "Method"}, {"text": "rotation", "type": "OtherScientificTerm"}, {"text": "translation", "type": "OtherScientificTerm"}, {"text": "non-rigid deformation", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "Reconstruction is ill-posed if arbitrary deformations are allowed .", "entities": [{"text": "Reconstruction", "type": "OtherScientificTerm"}, {"text": "arbitrary deformations", "type": "OtherScientificTerm"}], "relations": []}, {"sentence": "We constrain the problem by assuming that the object shape at each time instant is drawn from a Gaussian distribution .", "entities": [{"text": "object shape", "type": "OtherScientificTerm"}, {"text": "Gaussian distribution", "type": "Method"}], "relations": [{"head": "Gaussian distribution", "tail": "object shape", "label": "USED-FOR", "head_type": "Method", "tail_type": "OtherScientificTerm"}]}, {"sentence": "Based on this assumption , the algorithm simultaneously estimates 3D shape and motion for each time frame , learns the parameters of the Gaussian , and robustly fills-in missing data points .", "entities": [{"text": "algorithm", "type": "Generic"}, {"text": "3D shape and motion", "type": "OtherScientificTerm"}, {"text": "Gaussian", "type": "Method"}], "relations": [{"head": "algorithm", "tail": "3D shape and motion", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}]}, {"sentence": "We then extend the algorithm to model temporal smoothness in object shape , thus allowing it to handle severe cases of missing data .", "entities": [{"text": "algorithm", "type": "Generic"}, {"text": "temporal smoothness in object shape", "type": "OtherScientificTerm"}, {"text": "it", "type": "Generic"}, {"text": "missing data", "type": "Material"}], "relations": [{"head": "algorithm", "tail": "temporal smoothness in object shape", "label": "USED-FOR", "head_type": "Generic", "tail_type": "OtherScientificTerm"}, {"head": "it", "tail": "missing data", "label": "USED-FOR", "head_type": "Generic", "tail_type": "Material"}]}] |
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 5