Generating and evaluating domain-oriented multi-word terms from texts
Information Processing and Management: an International Journal
WordNet: a lexical database for English
Communications of the ACM
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
Applied morphological processing of English
Natural Language Engineering
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Deep vs. shallow semantic analysis applied to textual entailment recognition
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Discovering verb relations in corpora: distributional versus non-distributional approaches
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
A knowledge based strategy for recognising textual entailment
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
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In this work we investigate methods to enable the detection of a specific type of textual entailment (strict entailment), starting from the preliminary assumption that these relations are often clearly expressed in texts. Our method is a statistical approach based on what we call textual entailment patterns, prototypical sentences hiding entailment relations among two activities. We experimented the proposed method using the entailment relations of WordNet as test case and the web as corpus where to estimate the probabilities; obtained results will be shown.