ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification

doi:10.1016/j.neucom.2016.05.041

	ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification
	Fu, Hongping 1; Niu, Zhendong 1; Zhang, Chunxia 2; Yu, Hanchao 3; Ma, Jing 1; Chen, Jie 1; Chen, Yiqiang 3; Liu, Junfa 3
	2016-09-26
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	207 页码:599-609
摘要	Question subjectivity identification in Community Question Answering (CQA) has attracted a lot of attentions in recent years. With the rapid development of CQA, subjective questions posted by users are growing exponentially, which presents two challenges for question subjectivity identification. The first one is the data imbalance between subjective and objective questions. The second one is that the amount of manually labelled training data is hard to catch up with the fast developing speed of CQA. In this paper, we propose an adaptive semi-supervised Extreme Learning Machine (ASELM) to solve those two challenges. To resolve the data imbalance problem, ASELM employs the different impacts on identification performance caused by the imbalanced data. Second, the proposed method introduces the unlabelled data, and builds a model about the ratio between the number of labelled and unlabelled data based on Gaussian Model, which is applied to automatically generate the constraint on the unlabelled data. Experimental results showed ASELM improved identification performance for the imbalanced data, and outperformed the performance of basic ELM, SELM, Weighted ELM and SS-ELM on both F1 measure and accuracy. (C) 2016 Elsevier B.V. All rights reserved.
关键词	ELM Adaptive semi-supervised ELM Question subjectivity identification Community question answering
DOI	10.1016/j.neucom.2016.05.041
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61370137] ; National Natural Science Foundation of China[61272361] ; National Natural Science Foundation of China[61502456] ; National Natural Science Foundation of China[61572471] ; National Natural Science Foundation of China[61502033] ; Major Science and Technology Project of Press and Publication[GAPP ZDKJ BQ/01] ; Open Project from State Key Laboratory of Management and Control for Complex System (China)[99S9021F4D]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000382794500056
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8088
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Chunxia
作者单位	1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 2.Beijing Inst Technol, Sch Software, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Fu, Hongping,Niu, Zhendong,Zhang, Chunxia,et al. ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification[J]. NEUROCOMPUTING,2016,207:599-609.
APA	Fu, Hongping.,Niu, Zhendong.,Zhang, Chunxia.,Yu, Hanchao.,Ma, Jing.,...&Liu, Junfa.(2016).ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification.NEUROCOMPUTING,207,599-609.
MLA	Fu, Hongping,et al."ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification".NEUROCOMPUTING 207(2016):599-609.