Coupled Matrix Factorization for Question Similarity

WEI Chuyuan; LI Fangfang; ZHAN Qiang; FAN Xiongzhong

doi:10.1049/cje.2016.06.034

Volume 25 Issue 4

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WEI Chuyuan, LI Fangfang, ZHAN Qiang, et al., “Coupled Matrix Factorization for Question Similarity,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 665-671, 2016, doi: 10.1049/cje.2016.06.034

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WEI Chuyuan, LI Fangfang, ZHAN Qiang, et al., “Coupled Matrix Factorization for Question Similarity,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 665-671, 2016, doi: 10.1049/cje.2016.06.034

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Coupled Matrix Factorization for Question Similarity

doi: 10.1049/cje.2016.06.034

1.
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;
2.
Department of Computer, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
3.
Advanced Analytics Institute, University of Technology, Sydney, Australia

Funds: This work is supported by the National Natural Science Foundation of China (No.61371194, No.61462004), and the National Basic Research Program of China (973 Program) (No.2013CB329303).

Received Date: 2015-07-06
Rev Recd Date: 2015-10-19
Publish Date: 2016-07-10

Abstract

Abstract

Community question answering (CQA) has provided an increasingly popular service where users ask and answer questions and access historical question-answer pairs. As a fundamental task in CQA, question similarity measure is to compute the similarity between the queried question and the historical questions which have been solved by other users. We mine and use the most important semantic features as the semantic representation of questions, and try to incorporate the couplings of semantic features into vector space model. We propose Coupled question similarity (CQS) model, and compute the similarity in matrix factorization framework. Experiments conducted on real CQA data sets demonstrate that with the incorporation of such couplings, the performance of sentence similarity is improved compared to a variety of baseline methods significantly.
- Sentence similarity,
- Community question answering (CQA),
- Matrix factorization (MF),
- Coupled question similarity (CQS)

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References(26)

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