Entailment, intensionality and text understanding
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Recognising textual entailment with logical inference
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
Modeling semantic containment and exclusion in natural language inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
It's a contradiction---no, it's not: a case study using functional relations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A phrase-based alignment model for natural language inference
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Learning alignments and leveraging natural logic
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
Presupposed content and entailments in natural language inference
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
"Ask not what textual entailment can do for you..."
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Constraints based taxonomic relation classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
An analytic tableau system for natural logic
Proceedings of the 17th Amsterdam colloquium conference on Logic, language and meaning
Towards better ontological support for recognizing textual entailment
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Bridges from Language to Logic: Concepts, Contexts and Ontologies
Electronic Notes in Theoretical Computer Science (ENTCS)
Defining specialized entailment engines using natural logic relations
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
Unsupervised learning of semantic relation composition
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Integrating logical representations with probabilistic information using Markov logic
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Deriving rhetorical relationships from semantic content
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Generalizing monotonicity inferences to opposition inferences
AC'11 Proceedings of the 18th Amsterdam colloquim conference on Logic, Language and Meaning
The Soundness of Internalized Polarity Marking
Studia Logica
Inclusion and Exclusion in Natural Language
Studia Logica
Composition of semantic relations: Theoretical framework and case study
ACM Transactions on Speech and Language Processing (TSLP)
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We propose a model of natural language inference which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We extend past work in natural logic, which has focused on semantic containment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical semantic relation for each edit; propagates these relations upward through a semantic composition tree according to properties of intermediate nodes; and joins the resulting semantic relations across the edit sequence. A computational implementation of the model achieves 70% accuracy and 89% precision on the FraCaS test suite. Moreover, including this model as a component in an existing system yields significant performance gains on the Recognizing Textual Entailment challenge.