A Probabilistic Classification Approach for Lexical Textual Entailment
Abstract
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 Nave 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 Classification Approach for Lexical Textual Entailment." AAAI Conference on Artificial Intelligence, 2005.Markdown
[Glickman et al. "A Probabilistic Classification Approach for Lexical Textual Entailment." AAAI Conference on Artificial Intelligence, 2005.](https://mlanthology.org/aaai/2005/glickman2005aaai-probabilistic/)BibTeX
@inproceedings{glickman2005aaai-probabilistic,
title = {{A Probabilistic Classification Approach for Lexical Textual Entailment}},
author = {Glickman, Oren and Dagan, Ido and Koppel, Moshe},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2005},
pages = {1050-1055},
url = {https://mlanthology.org/aaai/2005/glickman2005aaai-probabilistic/}
}