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.
| Original language | English |
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| Title of host publication | Proceedings of the Main Conference and the Shared Task |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 237-245 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781937284213 |
| State | Published - 2012 |
| Event | 1st Joint Conference on Lexical and Computational Semantics, *SEM 2012 - Montreal, Canada Duration: 7 Jun 2012 → 8 Jun 2012 |
Publication series
| Name | *SEM 2012 - 1st Joint Conference on Lexical and Computational Semantics |
|---|---|
| Volume | 1 |
Conference
| Conference | 1st Joint Conference on Lexical and Computational Semantics, *SEM 2012 |
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| Country/Territory | Canada |
| City | Montreal |
| Period | 7/06/12 → 8/06/12 |
Bibliographical note
Publisher Copyright:© 2012 Association for Computational Linguistics.