融合抽象语义表示和依存语法的方面级情感分析

Sentiment Analysis with Abstract Meaning Representation and Dependency Grammar

【目的】充分融合自然语言句子的深层语义表示和表层语法结构,实现语义和语法的互补。【方法】提出基于规则串联的抽象语义表示和依存语法集成策略,并进行方面级情感分析。该策略利用回答集编程语言(ASP)将抽象语义表示、依存语法、词性分别表示为ASP事实,并基于抽象语义规则,通过规则体扩展的方式集成依存语法和词性,即将一个句子中的多种语言特征依次在规则体中进行串联来使用。该策略实现了两种方法:集成语义、语法和词性信息的AMR-DEP-POS-C方法;忽略词性信息的AMR-DEP-C方法。【结果】在8个公开评论数据集上的实验表明,AMR-DEP-POS-C方法能够实现语义和语法间的互补,比语义规则方法、语法规则方法以及基于深度学习的方法具有更好性能。【局限】该方法依赖于现有抽象语义表示和依存语法分析工具的准确性。【结论】AMR-DEP-POS-C方法具有可解释性,不需要大规模数据集,能够有效融合不同的语言特征,可为方面级情感分析任务带来新的研究视角和工具。

[Objective]This paper aims to combine the deep semantic representation and surface syntactic structure of natural language sentences.[Methods]We proposed an integration strategy based on semantic and syntactic rule concatenation and utilized it for the aspect-based sentiment analysis.This strategy used the answer set programming language(ASP)to represent abstract meaning representation(AMR),dependency grammar(DEP),and part of speech(POS)as ASP facts.It also integrated the DEP and POS through rule body extension based on AMR rules.Therefore,a sentence’s two or more language features were concatenated into the rule body.Based on this strategy,we developed the AMR-DEP-POS-C and AMR-DEP-C models.[Results]We examined the new methods on eight publicly available review datasets.The AMR-DEP-POS-C achieved a complementary relationship between semantics and syntax and performed better than the baseline methods based on semantic,syntactic,and deep learning.[Limitations]Our new models rely on the accuracy of the existing AMR and DEP parsers.[Conclusions]AMR-DEP-POS-C can effectively integrate different language features and bring new research perspectives and tools for aspect-based sentiment analysis.