论文标题
一种基于信息的简单方法，用于基于自适应的基于自适应的情感分析
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析（ABSA）是一项精细的情感分析任务，旨在从句子中提取方面并确定其相应的情感。方面术语提取（ATE）是ABSA的关键步骤。由于方面术语的昂贵注释，我们通常缺乏标记的目标域数据进行微调。为了解决这个问题，最近提出了许多方法来以一种无监督的方式转移常识，但是这种方法具有太多的模块，需要昂贵的多阶段预处理。在本文中，我们提出了一种基于相互信息最大化的简单但有效的技术，该技术可以作为增强跨域ABSA和ATE的任何类型模型的附加组件。此外，我们对这种方法提供了一些分析。实验结果表明，我们提出的方法的表现优于跨域ABSA的最先进方法，平均在10个不同的域对上平均是4.32％Micro-F1。除此之外，我们的方法可以扩展到其他序列标记任务，例如命名实体识别（NER）。
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspect term extraction (ATE) is the crucial step for ABSA. Due to the expensive annotation for aspect terms, we often lack labeled target domain data for fine-tuning. To address this problem, many approaches have been proposed recently to transfer common knowledge in an unsupervised way, but such methods have too many modules and require expensive multi-stage preprocessing. In this paper, we propose a simple but effective technique based on mutual information maximization, which can serve as an additional component to enhance any kind of model for cross-domain ABSA and ATE. Furthermore, we provide some analysis of this approach. Experiment results show that our proposed method outperforms the state-of-the-art methods for cross-domain ABSA by 4.32% Micro-F1 on average over 10 different domain pairs. Apart from that, our method can be extended to other sequence labeling tasks, such as named entity recognition (NER).
