Knowledge Commons of Institute of Automation,CAS
Multi-modal multi-view Bayesian semantic embedding for community question answering | |
Lei Sang1,2; Min Xu2; Shengsheng Qian3; Xindong Wu4 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2019-03-21 | |
卷号 | 334期号:1页码:44-58 |
摘要 | 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. |
关键词 | Community question answering Semantic embedding Multi-modal Multi-view Topic model Word embedding |
DOI | 10.1016/j.neucom.2018.12.067 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Innovative Research Team in University (PCSIRT) of the Ministry of Education[IRT17R32] ; National Key Research and Development Program of China[2016YFB1000901] ; National Key Research and Development Program of China[2016YFB1000901] ; Innovative Research Team in University (PCSIRT) of the Ministry of Education[IRT17R32] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000458626300005 |
出版者 | ELSEVIER SCIENCE BV |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25023 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Min Xu |
作者单位 | 1.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 |
推荐引用方式 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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