融合词性句法位置特征的汉老双语句子相似度计算  

Sentence Similarity Computation between Chinese and Lao with Part-of-Speech and Syntactic Position Information

作　　者：郭雷 周兰江[1] 周蕾越 GUO Lei;ZHOU Lanjiang;ZHOU Leiyue(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,Yunnan 650500,China;Faculty of Electrical and Information Engineering,Oxbridge College Kunming University of Science and Technology,Kunming,Yunnan 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500 [2]昆明理工大学津桥学院电气与信息工程学院,云南昆明650500

出　　处：《中文信息学报》2023年第12期76-86,共11页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61662040)。

摘　　要：汉语和老挝语句子表达存在较大的词序差异,在汉老平行句对语料中融入名词、形容词、量词、数词等词性的位置特征能有效提高句子相似度量的准确性。该文提出一种基于词性句法位置特征的相似度计算方法,首先向汉老双语句子添加特征词标签和特征词性标签使得句子的分布式表示包含更丰富的语义信息,然后利用含有自注意力机制(Self-Attention)的3个不同卷积核尺度的门控线性卷积网络(GCN)和双向长短时记忆网络(BiLSTM)分别挖掘汉老双语句子的深层语义信息,将两个网络输出的特征语义向量拼接,最后计算特征语义向量的相对差和相对积,将二者拼接并输入到全连接层得到汉老双语句子的相似度分数。实验结果表明,该文提出的方法在有限的语料下取得了更好的效果,F_1值达到了77.19%。Chinese and Lao sentences is different in word order.This paper proposes a similarity calculation method for Chinese and Lao based on part-of-speech and syntactic position information.First,the feature word tags and part-of-speech tags are inserted into the Chinese-Lao bilingual sentences before word embeddings are derived.Then the word embeddings of the any language are fed into three Gated Linear Convolutional Network(GCN)with different convolution kernel and a BiLSTM to get the sentence representation,respectively.The final sentence representation is the concatenation of the outputs of the self-attention mechanism applied respectively on the BiLSTM outputs and the concatenation of three GCN outputs.The sentence representations for each language are performed subtraction and dot production,respectively.The results are again concatenated as the input of the a fully connected layer for final decision.Experimental results show that the method proposed in this paper achieves better results under limited corpus,achieving 77.19%F 1 value.

关 键 词：汉语-老挝语 词性句法位置特征 门控线性卷积网络 双向长短期记忆网络 自注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]