Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental
task in opinion mining that aims to extract the targets (or aspects) on which
opinions have been expressed. Recent work focus on cross-domain OTE, which is
typically encountered in real-world scenarios, where the testing and training
distributions differ. Most methods use domain adversarial neural networks that
aim to reduce the domain gap between the labelled source and unlabelled target
domains to improve target domain performance. However, this approach only
aligns feature distributions and does not account for class-wise feature
alignment, leading to suboptimal results. Semi-supervised learning (SSL) has
been explored as a solution, but is limited by the quality of pseudo-labels
generated by the model. Inspired by the theoretical foundations in domain
adaptation [2], we propose a new SSL approach that opts for selecting target
samples whose model output from a domain-specific teacher and student network
disagree on the unlabelled target data, in an effort to boost the target domain
performance. Extensive experiments on benchmark cross-domain OTE datasets show
that this approach is effective and performs consistently well in settings with
large domain shifts.

本文提出了一种基于领域适应的半监督学习方法，该方法通过挑选目标样本来提高目标域的性能，并在基准跨域 OTE 数据集上进行了广泛的实验。

通过分类器差异进行自我训练以进行跨领域意见目标提取

Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction

This paper presents a supervised Aspect Based Sentiment Analysis (ABSA)
system. Our aim is to develop a modular platform which allows to easily conduct
experiments by replacing the modules or adding new features. We obtain the best
result in the Opinion Target Extraction (OTE) task (slot 2) using an
off-the-shelf sequence labeler. The target polarity classification (slot 3) is
addressed by means of a multiclass SVM algorithm which includes lexical based
features such as the polarity values obtained from domain and open polarity
lexicons. The system obtains accuracies of 0.70 and 0.73 for the restaurant and
laptop domain respectively, and performs second best in the out-of-domain
hotel, achieving an accuracy of 0.80.

本文介绍了一个监督式方面情感分析系统，使用模块化平台和词库解析进行实验，运用序列标注和多分类 SVM 算法实现评价目标提取、极性分类和评估，在餐厅和笔记本领域准确度分别为 0.70 和 0.73，并在 “酒店” 领域表现次佳，达到 0.80 的准确率。