Paper: Question Answering Using Enhanced Lexical Semantic Models

ACL ID P13-1171
Title Question Answering Using Enhanced Lexical Semantic Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

In this paper, we study the answer sentence selection problem for ques- tion answering. Unlike previous work, which primarily leverages syntactic analy- sis through dependency tree matching, we focus on improving the performance us- ing models of lexical semantic resources. Experiments show that our systems can be consistently and significantly improved with rich lexical semantic information, re- gardless of the choice of learning algo- rithms. When evaluated on a bench- mark dataset, the MAP and MRR scores are increased by 8 to 10 points, com- pared to one of our baseline systems using only surface-form matching. Moreover, our best system also outperforms pervious work that makes use of the dependency tree structure by a wide margin.