CASIA OpenIR
Multi-modal multi-view Bayesian semantic embedding for community question answering
Lei Sang1,2; Min Xu2; Shengsheng Qian3; Xindong Wu4
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-03-21
Volume334Issue:1Pages:44-58
Abstract

Semantic embedding has demonstrated its value in latent representation learning of data, and can be effectively adopted for many applications. However, it is difficult to propose a joint learning framework for semantic embedding in Community Question Answer (CQA), because CQA data have multi-view and sparse properties. In this paper, we propose a generic Multi-modal Multi-view Semantic Embedding (MMSE) framework via a Bayesian model for question answering. Compared with existing semantic learning methods, the proposed model mainly has two advantages: (1) To deal with the multi-view property, we utilize the Gaussian topic model to learn semantic embedding from both local view and global view. (2) To deal with the sparse property of question answer pairs in CQA, social structure information is incorporated to enhance the quality of general text content semantic embedding from other answers by using the shared topic distribution to model the relationship between these two modalities (user relationship and text content). We evaluate our model for question answering and expert finding task, and the experimental results on two real-world datasets show the effectiveness of our MMSE model for semantic embedding learning. (C) 2018 Published by Elsevier B.V.

KeywordCommunity question answering Semantic embedding Multi-modal Multi-view Topic model Word embedding
DOI10.1016/j.neucom.2018.12.067
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1000901] ; Innovative Research Team in University (PCSIRT) of the Ministry of Education[IRT17R32]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000458626300005
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25023
Collection中国科学院自动化研究所
Corresponding AuthorMin Xu
Affiliation1.Hefei Univ Technol, Hefei, Anhui, Peoples R China
2.Univ Technol Sydney, Sydney, NSW, Australia
3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
4.Univ Louisiana Lafayette, Lafayette, LA 70504 USA
Recommended Citation
GB/T 7714
Lei Sang,Min Xu,Shengsheng Qian,et al. Multi-modal multi-view Bayesian semantic embedding for community question answering[J]. NEUROCOMPUTING,2019,334(1):44-58.
APA Lei Sang,Min Xu,Shengsheng Qian,&Xindong Wu.(2019).Multi-modal multi-view Bayesian semantic embedding for community question answering.NEUROCOMPUTING,334(1),44-58.
MLA Lei Sang,et al."Multi-modal multi-view Bayesian semantic embedding for community question answering".NEUROCOMPUTING 334.1(2019):44-58.
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