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Statistical machine translation for query expansion in answer retrieval (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Vasserman, Er Mittal, Vibhu Tsochantaridis, Ioannis Liu, Yi Riezler, Stefan |
| Description | Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL’09 |
| Abstract | We present an approach to query expansion in answer retrieval that uses Statistical Machine Translation (SMT) techniques to bridge the lexical gap between questions and answers. SMT-based query expansion is done by i) using a full-sentence paraphraser to introduce synonyms in context of the entire query, and ii) by translating query terms into answer terms using a full-sentence SMT model trained on question-answer pairs. We evaluate these global, context-aware query expansion techniques on tfidf retrieval from 10 million question-answer pairs extracted from FAQ pages. Experimental results show that SMTbased expansion improves retrieval performance over local expansion and over retrieval without expansion. 1 |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Smtbased Expansion Query Term Lexical Gap Statistical Machine Translation Query Expansion Question-answer Pair Full-sentence Smt Model Tfidf Retrieval Full-sentence Paraphraser Context-aware Query Expansion Technique Faq Page Retrieval Performance Entire Query Answer Retrieval Experimental Result Smt-based Query Expansion Answer Term Local Expansion |
| Content Type | Text |
| Resource Type | Proceeding |
