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
Haoran Li1,2; Jiajun Zhang1,2; Chengqing Zong1,2,3
Source PublicationACM Transactions on Asian and Low-Resource Language Information Processing
ISSN2375-4699
2017
Volume16Issue:3Pages:1-21
Subtype长文
Abstract

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.

KeywordImplicit Discourse Relation Maxmargin Learning Neural Network Multilevel Features
Indexed BySSCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23107
Collection模式识别国家重点实验室_自然语言处理
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Haoran Li,Jiajun Zhang,Chengqing Zong. 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):1-21.
APA Haoran Li,Jiajun Zhang,&Chengqing Zong.(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),1-21.
MLA Haoran Li,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):1-21.
Files in This Item: Download All
File Name/Size DocType Version Access License
2TALLIP.pdf(753KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Haoran Li]'s Articles
[Jiajun Zhang]'s Articles
[Chengqing Zong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Haoran Li]'s Articles
[Jiajun Zhang]'s Articles
[Chengqing Zong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Haoran Li]'s Articles
[Jiajun Zhang]'s Articles
[Chengqing Zong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2TALLIP.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.