Discovery of inference rules for question-answering
Natural Language Engineering
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Alignment of shared forests for bilingual corpora
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
An inference model for semantic entailment in natural language
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Paraphrase substitution for recognizing textual entailment
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Answering questions with authority
Proceedings of the 17th ACM conference on Information and knowledge management
Inference rules and their application to recognizing textual entailment
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Extracting paraphrase patterns from bilingual parallel corpora
Natural Language Engineering
A machine learning approach to textual entailment recognition
Natural Language Engineering
Inference rules for recognizing textual entailment
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Multi-word expressions in textual inference: much ado about nothing?
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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This paper addresses syntax-based paraphrasing methods for Recognizing Textual Entailment (RTE). In particular, we describe a dependency-based paraphrasing algorithm, using the DIRT data set, and its application in the context of a straightforward RTE system based on aligning dependency trees. We find a small positive effect of dependency-based paraphrasing on both the RTE3 development and test sets, but the added value of this type of paraphrasing deserves further analysis.