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Title: Retrieving questions and answers in community-based question answering services
Authors: WANG KAI
Keywords: Question Answering, Question Retrieval, Question Segmentation, Answer Segmentation, Social Media
Issue Date: 28-Dec-2010
Citation: WANG KAI (2010-12-28). Retrieving questions and answers in community-based question answering services. ScholarBank@NUS Repository.
Abstract: While traditional question answering (QA) systems tailored to the TREC QA task work relatively well for simple questions, they do not suffice to answer real world questions. The community-based QA (cQA) systems offer this service well, as they contain large archives of such questions where manually crafted answers are directly available. However, the question and answer retrieval in the cQA archive is not trivial. In particular, I identify three major challenges in building up such a QA system ? (1) matching of complex online questions; (2) multi-sentence questions mixed with context sentences; and (3) mixture of sub-answers corresponding to sub-questions. To tackle these challenges, I focus my research in developing advanced techniques to deal with the complicated matching issues and the segmentation problems for cQA questions and answers, including: 1) A Syntactic Tree Matching model based on a comprehensive tree weighing scheme to flexibly match cQA questions at the lexical and syntactic levels to find similar questions; 2) A question segmentation model to differentiate sub-questions of different topics and align them to the corresponding context sentences; and 3) An answer segmentation model to model the question-answer relationships to segment multi-topic answer sentences and perform question-answer alignment. The main contributions of this thesis are in developing the syntactic tree matching model to flexibly match online questions coupled with various grammatical errors, and the segmentation models to sort out different sub-questions and sub-answers for better and more precise cQA retrieval. These models are generic in the sense that they can be applied to other related applications.
Appears in Collections:Ph.D Theses (Open)

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