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

文献类型：期刊论文

作者Fu, Hongping2; Niu, Zhendong2; Zhang, Chunxia1; Yu, Hanchao3; Ma, Jing2; Chen, Jie2; Chen, Yiqiang3; Liu, Junfa3
刊名NEUROCOMPUTING
出版日期2016-09-26
卷号207页码:599-609
关键词ELM Adaptive semi-supervised ELM Question subjectivity identification Community question answering
ISSN号0925-2312
DOI10.1016/j.neucom.2016.05.041
英文摘要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.
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
语种英语
WOS记录号WOS:000382794500056
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/8087]  
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 Software, Beijing 100081, Peoples R China
2.Beijing Inst Technol, Sch Comp Sci & Technol, 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.

入库方式： OAI收割

来源：计算技术研究所

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