融合方面语义和网格标记的多语言意见元组抽取

Multilingual Opinion Factor Extraction Fusing Aspect Semantics and Grid Tagging

面向方面的细粒度意见抽取(Aspect-oriented Fine-grained Opinion Extraction,AFOE)任务的目的是以意见对的形式抽取文本评论中的方面和意见词或者再抽取情感极性,形成意见三元组。以往的研究通常以管道方式抽取意见元素,容易出现错误传播的问题,而且大多数只关注方面词和意见词的单个子任务抽取,忽略了不同意见元素之间的相互影响和指示信息,导致意见挖掘任务不完整。此外,面向中文的意见元素抽取任务的研究较少。针对以上问题,文中提出了融合方面语义和网格标记的多语言意见元组抽取模型。首先,使用向内LSTM(Inward-LSTM)和向外LSTM(Outward-LSTM)编码方面词及其对应的上下文信息建立方面和候选意见词的关联,再结合全局信息生成特定方面语义特征的上下文表示,有利于提高下游意见元素抽取的性能。其次,使用网格标记方案的推理策略,利用方面和意见词之间的依赖指示信息进行更准确的抽取,以端到端的方式处理AFOE任务。相比基线模型,对于方面意见对抽取任务,改进的模型在中英文数据集上的F1值提高了0.89%~4.11%,对于三元组抽取任务提高了1.36%~3.11%,实验结果表明,改进的模型能有效地对中英文评论的意见元素进行抽取,性能显著优于基线模型。

Aspect-oriented fine-grained opinion extraction(AFOE)aims to extract the aspect and opinion terms in the reviews in the form of opinion pairs or to extract the sentiment polarity to form opinion triplets.Previous studies usually extract opinion factors in a pipeline manner,which is prone to the problem of error propagation,most of them only focus on the single sub-task extraction of aspect terms or opinion terms,and ignore the mutually interactive and indicative information between different opinion factors,which lead to the problem that opinion excavation tasks are incomplete.In addition,the existing researches do not pay attention to the research of Chinese-oriented opinion factors extraction.To tackle the problems,this paper proposes multilingual opinion factors extraction model fusing aspect semantics and grid tagging.Firstly,inward LSTM(Inward-LSTM)and outward LSTM(Outward-LSTM)are exploited to encode aspect terms and corresponding left-right contexts to establish the association between aspect and candidate opinion terms,and then combine global context information to generate contextualized representation of specific aspect semantic features,which is beneficial to improve the performance of downstream opinion factors extraction.Secondly,the inference strategy of the grid tagging scheme is applied to decode the potential indications between aspect and opi-nion terms for more accurate extraction,the AFOE task is handled in an end-to-end manner.Compared with the baseline model,the F1 scores of the proposed model in the Chinese and English datasets increase by 0.89%~4.11%for the aspect opinion pair extraction task,and 1.36%~3.11%for the triplet extraction task.Experimental results show that the improved model can effectively extract the opinion factors of Chinese and English comments,the performance is significantly better than the baseline model.