CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Implicit Discourse Relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning
Li, Haoran1; Zhang, Jiajun1; Zong, Chengqing2
Source PublicationACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
2017-04-01
Volume16Issue:3Pages:21
SubtypeArticle
AbstractDiscourse 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.
KeywordImplicit Discourse Relation Neural Network Multilevel Features Maxmargin Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1145/3028772
Indexed BySCI
Language英语
Funding OrganizationNatural Science Foundation of China(61333018 ; Strategic Priority Research Program of the CAS(XDB02070007) ; 91520204)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000399087800005
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15090
Collection模式识别国家重点实验室_自然语言处理
Affiliation1.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
Recommended Citation
GB/T 7714
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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