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Modeling and learning distributed word representation with metadata for question retrieval

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成果类型:
期刊论文
作者:
Zhou, Guangyou*;Huang, Jimmy Xiangji
通讯作者:
Zhou, Guangyou
作者机构:
[Huang, Jimmy Xiangji; Zhou, Guangyou] Cent China Normal Univ, Sch Comp, Wuhan 430072, Hubei, Peoples R China.
[Huang, Jimmy Xiangji; Zhou, Guangyou] York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada.
通讯机构:
[Zhou, Guangyou] C
[Zhou, Guangyou] Y
Cent China Normal Univ, Sch Comp, Wuhan 430072, Hubei, Peoples R China.
York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada.
语种:
英文
关键词:
community question answering;information retrieval;Natural language processing;question retrieval;text mining
期刊:
IEEE Transactions on Knowledge and Data Engineering
ISSN:
1041-4347
年:
2017
卷:
29
期:
6
页码:
1226-1239
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61573163]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [CCNU16A02024]; Wuhan Youth Science and Technology plan; Natural Sciences and Engineering Research Council (NSERC) of CanadaNatural Sciences and Engineering Research Council of Canada (NSERC); NSERC CREATE award in ADERSIM; York Research Chairs (YRC) program; Ontario Research Fund-Research Excellence (ORF-RE) award in BRAIN Alliance
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA...

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