@inproceedings{5dd1aff09f0543b5b61124661edda1b8,
title = "A probabilistic lexical model for ranking textual inferences",
abstract = "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.",
author = "Eyal Shnarch and Ido Dagan and Jacob Goldberger",
note = "Publisher Copyright: {\textcopyright} 2012 Association for Computational Linguistics.; 1st Joint Conference on Lexical and Computational Semantics, *SEM 2012 ; Conference date: 07-06-2012 Through 08-06-2012",
year = "2012",
language = "الإنجليزيّة",
series = "*SEM 2012 - 1st Joint Conference on Lexical and Computational Semantics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "237--245",
booktitle = "Proceedings of the Main Conference and the Shared Task",
address = "الولايات المتّحدة",
}