论文标题
将句法结构引入目标意见词提取和深度学习
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning
论文作者
论文摘要
有针对性的意见词提取（TOWE）是基于方面的情感分析（ABSA）的子任务，旨在在句子中找到给定方面的意见单词。尽管他们在Towe方面取得了成功，但目前的深度学习模型未能利用被证明对Towe在先前的研究中很有用的句子的句法信息。在这项工作中，我们建议将这些句子的句法结构纳入towe的深度学习模型中，利用基于语法的意见可能性得分和两个单词之间的句法联系。我们还介绍了一种新颖的正则化技术，以根据TOWE单词之间的表示区分来提高深度学习模型的性能。对所提出的模型进行了广泛的分析，并在四个基准数据集上实现了最先进的性能。
Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior research. In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words. We also introduce a novel regularization technique to improve the performance of the deep learning models based on the representation distinctions between the words in TOWE. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
