期刊论文

Relation Classification of Non-Saturated Chinese Compound Sentence via Feature Fusion CNN

中文

计算机系统应用

1003-3254

2020

29

6

224-229

10.15888/j.cnki.csa.007369

国家社科基金（19BYY092）~~；

本校为第一机构

汉语文章中复句占多数,复句关系类别的识别是对复句分句之间的语义关系的甄别,是分析复句语义的关键.在关系词非充盈态复句中,部分关系词缺省,因此,不能通过关系词搭配的规则来对非充盈态复句进行类别识别,且通过人工分析分句的特征进行类别识别费时费力.本文以二句式非充盈态复句为研究对象,采用在卷积神经网络中融合关系词特征的FCNN模型,尽可能减少对语言学知识和语言规则的依赖,通过学习自动分析两个分句之间语法语义等特征,从而识别出复句的关系类别.使用本文提出的方法对复句关系类别识别准确率达97%,实验结果证明了该方法的有效性.

In Chinese essay,compound sentences are the majority.Recognition of relation category is screening for semantic relation of clauses in a compound sentence,and it is the key to analyze the meaning of the whole compound sentences.In a non-saturated compound sentence,the relation words are absent.So,the non-saturated compound sentence can not be classified by the features of the relation word collocation.In this work,an unbalanced corpus of non-saturated compound sentences with two clauses is taken as the research object.This study proposes a convolutional neural network for relation classificati...