Paper: Automatically Mining Question Reformulation Patterns from Search Log Data

ACL ID P12-2037
Title Automatically Mining Question Reformulation Patterns from Search Log Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

Natural language questions have become pop- ular in web search. However, various ques- tions can be formulated to convey the same information need, which poses a great chal- lenge to search systems. In this paper, we au- tomatically mined 5w1h question reformula- tion patterns from large scale search log data. The question reformulations generated from these patterns are further incorporated into the retrieval model. Experiments show that us- ing question reformulation patterns can sig- nificantly improve the search performance of natural language questions.