A Probabilistic Lexical Model for Ranking Textual Inferences
Eyal Shnarch, Ido Dagan, Jacob H. Goldberger · 2012
Identifying textual inferences, where the meaning of one text follows from another, is a general underlying task within many natu-ral language applications. Commonly, it is ap-proached either by generative syntactic-based methods or by “lightweight ” heuristic lexical models. We suggest a model which is confined to simple lexical information, but is formu-lated as a principled generative probabilistic model. We focus our attention on the task of ranking textual inferences and show substan-tially improved results on a recently investi-gated question answering data set. 1