Question-answer topic model for question retrieval in community question answering
- 29 October 2012
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 2471-2474
- https://doi.org/10.1145/2396761.2398669
Abstract
The major challenge for Question Retrieval (QR) in Community Question Answering (CQA) is the lexical gap between the queried question and the historical questions. This paper proposes a novel Question-Answer Topic Model (QATM) to learn the latent topics aligned across the question-answer pairs to alleviate the lexical gap problem, with the assumption that a question and its paired answer share the same topic distribution. Experiments conducted on a real world CQA dataset from Yahoo! Answers show that combining both parts properly can get more knowledge than each part or both parts in a simple mixing way and combining our QATM with the state-of-the-art translation-based language model, where the topic and translation information is learned from the question-answer pairs at two different grained semantic levels respectively, can significantly improve the QR performance.Keywords
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