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Answer Re-ranking with bilingual LDA and social QA forum corpus

Katsura, Akihiro (2014) Answer Re-ranking with bilingual LDA and social QA forum corpus. MSc by research thesis, University of York.

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One of the most important tasks for AI is to find valuable information from the Web. In this research, we develop a question answering system for retrieving answers based on a topic model, bilingual latent Dirichlet allocation (Bi-LDA), and knowledge from social question answering (SQA) forum, such as Yahoo! Answers. Regarding question and answer pairs from a SQA forum as a bilingual corpus, a shared topic over question and answer documents is assigned to each term so that the answer re-ranking system can infer the correlation of terms between questions and answers. A query expansion approach based on the topic model obtains a 9% higher top-150 mean reciprocal rank (MRR@150) and a 16% better geometric mean rank as compared to a simple matching system via Okapi/BM25. In addition, this thesis compares the performance in several experimental settings to clarify the factor of the result.

Item Type: Thesis (MSc by research)
Academic Units: The University of York > Computer Science (York)
Depositing User: Mr Akihiro Katsura
Date Deposited: 24 Nov 2014 17:02
Last Modified: 24 Nov 2014 17:02
URI: http://etheses.whiterose.ac.uk/id/eprint/7358

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