Query expansion using lexical-semantic relations
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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NAACL-Demonstrations '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Demonstrations - Volume 4
HLT '93 Proceedings of the workshop on Human Language Technology
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ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
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RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Journal of Artificial Intelligence Research
Directional distributional similarity for lexical inference
Natural Language Engineering
Types of common-sense knowledge needed for recognizing textual entailment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Towards a probabilistic model for lexical entailment
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
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Lexical-semantic resources are used extensively for applied semantic inference, yet a clear quantitative picture of their current utility and limitations is largely missing. We propose system- and application-independent evaluation and analysis methodologies for resources' performance, and systematically apply them to seven prominent resources. Our findings identify the currently limited recall of available resources, and indicate the potential to improve performance by examining non-standard relation types and by distilling the output of distributional methods. Further, our results stress the need to include auxiliary information regarding the lexical and logical contexts in which a lexical inference is valid, as well as its prior validity likelihood.