|Table of Contents|

A Hybrid Tree Structured Neural Network for Implicit Discourse Relation Recognition(PDF)

Journal of Xiamen University(Natural Science)[ISSN:0438-0479/CN:35-1070/N]

Issue:
2017 04
Page:
576-583
Research Field:
Research Articles
Publishing date:
2017-07-26

Info

Title:
A Hybrid Tree Structured Neural Network for Implicit Discourse Relation Recognition
Article ID:
0438-0479(2017)04-0576-08
Author(s):
ZHENG JianglongCHEN Jingxiu*
School of Information Science and Engineering,Xiamen University,Xiamen 361005,China
Keywords:
implicit discourse relation recognition specific information tree-structured long short-term memory(Tree-LSTM) neural tensor network(NTN)
CLC number:
TP 391
DOI:
10.6043/j.issn.0438-0479.201701010
Document code:

A
Abstract:
The most critural challenge of implicit discourse relation recognition lies in how to represent the semantic information of each discourse argument.However,the semantic value of the sentence is mainly decided by its specific information focus in linguistics.Therefore,the discourse relation may mostly depend on links between information focuses.Intuitively,we cannot give equal treatment to every phrase branches during composition up the syntactic parse tree.To resolve the problem,we introduce the tree-structured long short-term memory(Tree-LSTM)network to selectively incorporate information from each child to compute the distributed semantic representation of two arguments.Consequently,it can emphasize those informative predicative branches that indicate the "focus" of a sentence.Then the neural tensor network(NTN)is used to predict the semantic correlation between these two discourse arguments across multiple dimensions.Experimental results on PDTB corpus show that our model has achieved some improvement on the task of discourse relation recognition.

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Memo

Memo:
收稿日期:2017-01-09 录用日期:2017-05-11
基金项目:国家自然科学基金(60803078); 福建省自然科学基金(2010J01351); 教育部海外留学回国人员科研启动基金
*通信作者:cjx@xmu.edu.cn
Last Update: 1900-01-01