Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval |
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| Authors: | Yu Zhang Wei-Nan Zhang Ke Lu Rongrong Ji Fanglin Wang Ting Liu |
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| Institution: | 1. Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin City, Heilongjiang, China.; 2. Graduate University of Chinese Academy of Sciences, Beijing City, China.; 3. Department of Cognitive Science, Xiamen University, Xiamen City, Fujian, China.; 4. School of Computing, National University of Singapore, Singapore, Singapore.; University of Adelaide, Australia, |
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| Abstract: | Lexical gap in cQA search, resulted by the variability of languages, has been recognized as an important and widespread phenomenon. To address the problem, this paper presents a question reformulation scheme to enhance the question retrieval model by fully exploring the intelligence of paraphrase in phrase-level. It compensates for the existing paraphrasing research in a suitable granularity, which either falls into fine-grained lexical-level or coarse-grained sentence-level. Given a question in natural language, our scheme first detects the involved key-phrases by jointly integrating the corpus-dependent knowledge and question-aware cues. Next, it automatically extracts the paraphrases for each identified key-phrase utilizing multiple online translation engines, and then selects the most relevant reformulations from a large group of question rewrites, which is formed by full permutation and combination of the generated paraphrases. Extensive evaluations on a real world data set demonstrate that our model is able to characterize the complex questions and achieves promising performance as compared to the state-of-the-art methods. |
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