Implicit Discourse Relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning

doi:10.1145/3028772

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Haoran; Zhang, Jiajun; Zong, Chengqing
刊名	ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
	2017-04-01
卷号	16 期号:3 页码:21
关键词	Implicit Discourse Relation Neural Network Multilevel Features Maxmargin Learning
DOI	10.1145/3028772
文献子类	Article
英文摘要	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.
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000399087800005
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40802]
专题	模式识别国家重点实验室_自然语言处理
推荐引用方式 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.