论文标题
使用深层上下文单词嵌入和分层关注的基于方面情感分析的混合方法
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention
论文作者
论文摘要
网络已成为人们对感兴趣实体及其相关方面的看法的主要平台。基于方面的情感分析（ABSA）旨在自动计算对这些方面的情感。在本文中，我们将基于方面情感分析（HAABSA）方法的最新混合方法扩展到两个方向。首先，我们用深层上下文单词嵌入替换非上下文单词嵌入，以便更好地应对给定文本中的语义。其次，我们通过在HAABSA高级表示中添加额外的注意力层来使用层次的关注，以提高对输入数据建模的方法灵活性。使用两个标准数据集（Semeval 2015和Semeval 2016），我们表明所提出的扩展名提高了ABSA构建模型的准确性。
The Web has become the main platform where people express their opinions about entities of interest and their associated aspects. Aspect-Based Sentiment Analysis (ABSA) aims to automatically compute the sentiment towards these aspects from opinionated text. In this paper we extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two directions. First we replace the non-contextual word embeddings with deep contextual word embeddings in order to better cope with the word semantics in a given text. Second, we use hierarchical attention by adding an extra attention layer to the HAABSA high-level representations in order to increase the method flexibility in modeling the input data. Using two standard datasets (SemEval 2015 and SemEval 2016) we show that the proposed extensions improve the accuracy of the built model for ABSA.
