摘要
排比句具有结构紧凑、句式整齐、富有表现力等鲜明的特点,广泛应用在各种文体之中,在近几年语文高考的鉴赏类问题中也多有考察,但在自动识别方面的研究还鲜有涉及。该文依据排比句结构相似、内容相关的特点,以句子的词性、词语作为基本特征,设计了融合卷积神经网络和结构相似度计算的排比句识别方法。首先将词向量和词性向量融入句子的分布式表示中,利用多个卷积核对其进行卷积操作,设计出基于卷积神经网络的排比句识别方法。利用分句之间的词性串构造相似度计算,设计了基于结构相似度计算的排比句识别方法。同时考虑句子内部的语义相关性和结构相似性,将卷积神经网络和结构相似度计算方法融合,用于排比句的识别。对文学作品数据集和高考题中的文学类阅读材料数据集进行排比句识别实验,验证了该文所提的方法是有效的。
Parallelism has the advantages of compact structure,neat sentence,expressiveness and other distinctive features in all kinds of literary forms.In recent years,parallelism has also been found as the problem of appreciation in the Chinese college entrance examination,but the research of automatic recognition is rarely touched.In this paper,according to the characteristics of the similar syntactic structure and content relevance in parallelism,we design a method of combining the convolutional neural network and the structure similarity to recognition parallelism.We first use the word embedding and the vector of part-of-speech as the sentence distributed representation,employing multiple convolution kernels to execute the convolution operation,so as to realize the parallelism recognition method based on convolutional neural network.Using the parts of speech of the clauses string to create similarity calculation,we then emplement the parallelism recognition based on structure similarity calculation.Taking account of the semantic relevance and the structure similarity of the sentences,we combine the two methods to recognize parallelism.The experimental results show that the proposed recognition parallelism method is effective in the literature dataset and literature reading material datasets of the Chinese college entrance examination.
作者
穆婉青
廖健
王素格
MU Wanqing;LIAO Jian;WANG Suge(School of Computer ＆ Information Technology, .Shanxi University, Taiyuan, Shanxi 030006, China;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, China)
出处
《中文信息学报》
CSCD
北大核心
2018年第2期139-146,共8页
Journal of Chinese Information Processing
基金
国家"863"高技术项目(2015AA015407)
国家自然科学基金(61573231)
关键词
排比句
语义相关性
结构相似性
卷积神经网络
parallelism
semantic relevance
structure of the sentence similarity
convolutional neural network
