一种针对对话文本属性级情感信息抽取的词对关系建模方法  

A word-pair relationship modeling method for aspect-based sentiment information extraction in dialogue text

作　　者：曾涛 王晶晶[1] 张涵 刘一丁 ZENG Tao;WANG Jing-jing;ZHANG Han;LIU Yi-ding(School of Computer Science&Technology,Soochow University,Suzhou 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006

出　　处：《计算机工程与科学》2024年第12期2239-2251,共13页Computer Engineering & Science

摘　　要：属性级情感分析旨在获取文本中包含的细粒度情感信息,因其应用广泛而备受关注。然而传统的属性级情感分析研究大多基于非交互场景下的普通评价文本,针对对话文本的交互式场景下属性级情感分析的研究工作则非常稀缺。基于此现状,提出了针对对话文本交互式场景下的属性级情感信息联合抽取任务,获取由目标属性、意见表达以及意见对应的情感极性构成的完整的细粒度情感信息三元组,旨在通过一个任务获取交互式对话中最后一条发言包含的完整细粒度情感信息。针对该任务设计了一种端到端的基于词对关系建模的抽取方法,对词对间关系进行建模后将对话文本映射成一个有向图,将解码过程转换为在有向图中寻找特定环结构的过程。为了提升词对关系建模的准确率,设计了一种新颖的模型结构,在构建词对关系表征时融合词对相对距离信息与对话轮次信息,并通过多粒度二维卷积加强词对间的信息交互。此外,设计了一种动态损失权重方法,有效缓解了对话文本中词对关系类别分布不平衡问题。实验结果显示,本文方法与选用的强基线方法对比,F 1分数平均提升了7.70%,最高提升了15.05%。Aspect-based sentiment analysis aims to capture fine-grained sentiment information contained in text and has drawn considerable attention due to its wide applications.However,traditional research in aspect-based sentiment analysis predominantly relies on non-interactive review texts,with limited investigation into aspect-based sentiment analysis within interactive dialogue contexts.Addressing this gap,this paper proposes a joint extraction task for aspect-based sentiment information in interactive dialogue scenarios.The task aims to extract complete fine-grained sentiment information triplets consisting of target aspects,opinion expressions,and corresponding sentiment polarities,thereby obtaining comprehensive sentiment information from the final utterance in an interactive dialogue.To this end,this paper devises an end-to-end extraction method based on word-pair relation modeling,where in the relationship between word pairs are modeled to map dialogue text onto a directed graph,transforming the decoding process into a search for specific cyclic structures within the graph.To enhance the accuracy of word-pair relationship modeling,this paper introduces a novel model architecture that integrates relative distance information and dialogue turn information when constructing word-pair relationship representations,and utilizes multi-granularity 2D convolution to enhance interaction between word pairs.Additionally,this paper proposes a dynamic loss weighting method to effectively mitigate the issue of imbalanced category distributions in word-pair relation within dialogue texts.Experimental results demonstrate that,the proposed method outperforms strong baseline methods,achieving an average F 1 score improvement of 7.70%and a maximum improvement of 15.05%.

关 键 词：属性级情感分析 细粒度情感信息抽取 对话文本 词对关系建模 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]