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Multi-Channel CNN Based Inner-Attention for Compound Sentence Relation Classification

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成果类型:
期刊论文
作者:
Sun, Kaili;Li, Yuan;Deng, Dunhua;Li, Yang*
通讯作者:
Li, Yang
作者机构:
[Li, Yuan; Sun, Kaili; Li, Yang] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
[Deng, Dunhua] Cent China Normal Univ, Res Ctr Language & Language Educ, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Li, Yang] C
Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
Compounds;Feature extraction;Semantics;Task analysis;Computer architecture;Neural networks;Deep learning;Relation classification;multi-channel CNN;inner-attention mechanism;Chinese compound sentence without connectives
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2019
卷:
7
页码:
141801-141809
基金类别:
This work was supported in part by the National Social Science Fund of China under Grant 18BYY174, and in part by the Ministry of Education Humanities and Social Sciences Research Planning Fund of China under Grant 14YJA740020.
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Relation classification is a vital task in natural language processing, and it is screening for semantic relation between clauses in texts. This paper describes a study of relation classification on Chinese compound sentences without connectives. There exists an implicit relation in a compound sentence without connectives, which makes it difficult to realize the recognition of relation. The major challenges that relation classification modeling faces are how to obtain the contextual representation of sentence and relation dependence features between clauses. To solve this problem, we propose a...

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