A Probabilistic Lexical Model for Ranking Textual Inferences
نویسندگان
چکیده
Identifying textual inferences, where the meaning of one text follows from another, is a general underlying task within many natural language applications. Commonly, it is approached 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 formulated as a principled generative probabilistic model. We focus our attention on the task of ranking textual inferences and show substantially improved results on a recently investigated question answering data set.
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