A Probabilistic Lexical Approach to Textual Entailment
Oren Glickman, Ido Dagan and Moshe Koppel
In: IJCAI 2005, 30 JULY - 5 AUGUST 2005, EDINBURGH, SCOTLAND.
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.