A Probabilistic Classification Approach for Lexical Textual Entailment
Oren Glickman, Ido Dagan and Moshe Koppel
In: AAAI 2005, 9-13 JUL 2005, Pittsburgh, Pennsylvania, USA.
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.