A Probabilistic Lexical Approach to Textual Entailment
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 Naive 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.
Cite
Text
Glickman et al. "A Probabilistic Lexical Approach to Textual Entailment." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Glickman et al. "A Probabilistic Lexical Approach to Textual Entailment." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/glickman2005ijcai-probabilistic/)BibTeX
@inproceedings{glickman2005ijcai-probabilistic,
title = {{A Probabilistic Lexical Approach to Textual Entailment}},
author = {Glickman, Oren and Dagan, Ido and Koppel, Moshe},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {1682-1683},
url = {https://mlanthology.org/ijcai/2005/glickman2005ijcai-probabilistic/}
}