ESM:一种增强语义匹配的语句评分模型  

ESM:A sentence scoring model enhancing semantic matching

作　　者：曹小鹏 邵一萌 CAO Xiao-peng;SHAO Yi-meng(School of Computer,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学计算机学院,陕西西安710121

出　　处：《计算机工程与科学》2020年第6期1134-1140,共7页Computer Engineering & Science

基　　金：国家自然科学基金(61136002);陕西省工业公关项目(2014k06-36);西安市科技计划项目(CX12188(7));陕西省教育厅科技计划项目(2013JK1128)。

摘　　要：语义匹配问题是自然语言处理的核心问题之一。基于语义的匹配,即通过提取文本内在语义进行匹配度计算,是目前自然语言处理领域研究的热点。传统的语义匹配模型并没有结合语句通顺度等多种要素综合评价,因此效果较差。提出一种增强语义匹配模型,模型在文本相似度计算的基础上,增加通顺度因子,并通过大量数据来调整最优参数。通过自动阅卷系统进行测试,对比3种常用的自动阅卷评分模型验证该模型能有效降低平均误差值。The problem of semantic matching is one of the core problems in natural language proces-sing.Semantic based matching,that is,calculating the degree of matching by extracting the intrinsic semantics of the text,is a hot topic in the field of natural language processing.The traditional semantic matching model does not combine sentence smoothness and other factors to perform comprehensive eva-luation,so the effect is poor.This paper proposes an enhanced semantic matching model.Based on the text similarity calculation,the model adds a smoothness factor and adjusts the optimal parameters through a large amount of data.Through the test of the automatic marking system,three commonly used automatic marking models are compared to verify that the proposed model can effectively reduce the average error value.

关 键 词：文本相似度 统计语言 语义匹配 自动阅卷 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]