A Probabilistic Lexical Approach to Textual Entailment
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1682-1682.
Abstract
The textual entailment problem is to determine if a given text entails a given hypothesis. 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-theart heuristic scoring approaches.
@inproceedings{ijcai2005p272,
title = {A Probabilistic Lexical Approach to Textual Entailment},
author = {Glickman, Oren and Dagan, Ido and Koppel, Moshe},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1682--1682},
year = {2005},
url = {https://ijcai.org/proceedings/2005/272},
}
TY - CPAPER TI - A Probabilistic Lexical Approach to Textual Entailment AU - Glickman, Oren AU - Dagan, Ido AU - Koppel, Moshe T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1682 EP - 1682 UR - https://ijcai.org/proceedings/2005/272 ER -Copied
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