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Implicit Discourse Relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning
Li, Haoran1; Zhang, Jiajun1; Zong, Chengqing2
2017-04-01
发表期刊ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
卷号16期号:3页码:21
文章类型Article
摘要Discourse relations between two text segments play an important role inmany Natural Language Processing (NLP) tasks. The connectives strongly indicate the sense of discourse relations, while in fact, there are no connectives in a large proportion of discourse relations, that is, implicit discourse relations. Compared with explicit relations, implicit relations are much harder to detect and have drawn significant attention. Until now, there have been many studies focusing on English implicit discourse relations, and few studies address implicit relation recognition in Chinese even though the implicit discourse relations in Chinese are more common than those in English. In our work, both the English and Chinese languages are our focus. The key to implicit relation prediction is to properly model the semantics of the two discourse arguments, as well as the contextual interaction between them. To achieve this goal, we propose a neural network based framework that consists of two hierarchies. The first one is the model hierarchy, in which we propose a maxmargin learning method to explore the implicit discourse relation from multiple views. The second one is the feature hierarchy, in which we learn multilevel distributed representations from words, arguments, and syntactic structures to sentences. We have conducted experiments on the standard benchmarks of English and Chinese, and the results show that compared with several methods our proposed method can achieve the best performance in most cases.
关键词Implicit Discourse Relation Neural Network Multilevel Features Maxmargin Learning
WOS标题词Science & Technology ; Technology
DOI10.1145/3028772
收录类别SCI
语种英语
项目资助者Natural Science Foundation of China(61333018 ; Strategic Priority Research Program of the CAS(XDB02070007) ; 91520204)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000399087800005
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15090
专题模式识别国家重点实验室_自然语言处理
作者单位1.Chinese Acad Sci, Univ Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Intelligence Bldg 95,Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Univ Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Inst Automat,Natl Lab Pattern Recognit, Intelligence Bldg 95,Zhongguancun East Rd, Beijing 100190, Peoples R China
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Li, Haoran,Zhang, Jiajun,Zong, Chengqing. Implicit Discourse Relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2017,16(3):21.
APA Li, Haoran,Zhang, Jiajun,&Zong, Chengqing.(2017).Implicit Discourse Relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,16(3),21.
MLA Li, Haoran,et al."Implicit Discourse Relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 16.3(2017):21.
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