论文标题
改善基于方面情感分析的BERT绩效
Improving BERT Performance for Aspect-Based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析（ABSA）研究消费者对市场产品的看法。它涉及检查产品评论中表达的情感类型以及情感目标。分析评论中使用的语言是一项艰巨的任务，需要对语言有深入的了解。近年来，在这方面，深层语言模型（例如bert \ cite {devlin2019bert}）在这方面表现出了很大的进步。在这项工作中，我们提出了两个简单的模块，称为平行聚合和分层聚合，用于在BERT顶部用于两个主要的ABSA任务，即，提取（AE）和方面情感分类（ASC），以提高模型的性能。我们表明，应用所提出的模型消除了对BERT模型的进一步培训的必要性。源代码可在网络上获得，以进一步研究和复制结果。
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in product reviews. Analyzing the language used in a review is a difficult task that requires a deep understanding of the language. In recent years, deep language models, such as BERT \cite{devlin2019bert}, have shown great progress in this regard. In this work, we propose two simple modules called Parallel Aggregation and Hierarchical Aggregation to be utilized on top of BERT for two main ABSA tasks namely Aspect Extraction (AE) and Aspect Sentiment Classification (ASC) in order to improve the model's performance. We show that applying the proposed models eliminates the need for further training of the BERT model. The source code is available on the Web for further research and reproduction of the results.
