Artificial Intelligence - Special volume on natural language processing
An inferential approach to information retrieval and its implementation using a manual thesaurus
Artificial Intelligence Review
Meaning and grammar (2nd ed.): an introduction to semantics
Meaning and grammar (2nd ed.): an introduction to semantics
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
Discovery of inference rules for question-answering
Natural Language Engineering
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Entailment, intensionality and text understanding
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Investigating lexical substitution scoring for subtitle generation
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Lexical reference: a semantic matching subtask
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Inferring textual entailment with a probabilistically sound calculus*
Natural Language Engineering
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
Highlighting disputed claims on the web
Proceedings of the 19th international conference on World wide web
Good question! Statistical ranking for question generation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A semi-supervised method to learn and construct taxonomies using the web
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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
A lexical alignment model for probabilistic textual entailment
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
VENSES – a linguistically-based system for semantic evaluation
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Towards a probabilistic model for lexical entailment
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
Lexical entailment for information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Learning causality for news events prediction
Proceedings of the 21st international conference on World Wide Web
Mining the web to predict future events
Proceedings of the sixth ACM international conference on Web search and data mining
Learning to predict from textual data
Journal of Artificial Intelligence Research
An investigation into the application of ensemble learning for entailment classification
Information Processing and Management: an International Journal
Tailoring the automated construction of large-scale taxonomies using the web
Language Resources and Evaluation
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The textual entailment task - determining if a given text entails a given hypothesis - provides an abstraction of applied semantic inference. This paper describes first a general generative probabilistic setting for textual entailment. We then focus on the sub-task of recognizing whether the lexical concepts present in the hypothesis are entailed from the text. This problem is recast as one of text categorization in which the classes are the vocabulary words. We make novel use of Naïve Bayes to model the problem in an entirely unsupervised fashion. Empirical tests suggest that the method is effective and compares favorably with state-of-the-art heuristic scoring approaches.