论文标题
意见传输网络共同改善面向方面的意见单词提取和情感分类
Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification
论文作者
论文摘要
方面级别的情感分类（ALSC）和面向方面的意见单词提取（AOWE）是两个高度相关的基于方面的情感分析（ABSA）子任务。他们分别旨在检测情绪极性，并将相应的意见单词提取到句子中给定的方面。以前的作品将它们分开，并通过对小标记数据进行训练神经模型的重点，同时忽略它们之间的连接。在本文中，我们提出了一种新颖的联合模型，即意见传输网络（OTN），以利用ALSC和AOWE之间的潜在桥梁，以实现同时促进它们的目标。具体而言，我们设计了两种量身定制的意见传输机制，以分别从ALSC到AOWE和AOWE到ALSC。两个基准数据集的实验结果表明，我们的联合模型在这两个任务上的表现优于强大的基准。进一步的分析还验证了意见传播机制的有效性。
Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously. Specifically, we design two tailor-made opinion transmission mechanisms to control opinion clues flow bidirectionally, respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two benchmark datasets show that our joint model outperforms strong baselines on the two tasks. Further analysis also validates the effectiveness of opinion transmission mechanisms.
