A discriminative matching approach to word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to recognize features of valid textual entailments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A probabilistic setting and lexical cooccurrence model for textual entailment
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Textual entailment through extended lexical overlap and lexico-semantic matching
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
A compact forest for scalable inference over entailment and paraphrase rules
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Learning to predict from textual data
Journal of Artificial Intelligence Research
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In this paper, we introduce a new framework for recognizing textual entailment (RTE) which depends on extraction of the set of publicly-held beliefs -- known as discourse commitments -- that can be ascribed to the author of a text (t) or a hypothesis (h). We show that once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. Our system correctly identified entailment relationships in more than 80% of t-h pairs taken from all three of the previous PASCAL RTE Challenges, without the need for additional sources of training data.