BERT-ASC：用于情感分析中隐式方面学习的辅助句子构建
BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis
AI总结：
本文提出BERT-ASC统一框架，通过构建辅助句子机制解决隐式方面分类与情感分析任务，在多个基准数据集上取得最先进性能。
摘要：
基于方面的情感分析（ABSA）旨在将文本与一组方面相关联，并推断它们各自的情感极性。最先进的方法基于微调预训练语言模型构建，专注于从语料库中学习特定方面的表示。然而，方面通常是隐式表达的，在没有足够标注样本的情况下使得隐式映射具有挑战性，而这些样本在真实场景中可能很稀缺。本文提出了一个统一框架来解决方面分类和基于方面的情感子任务。我们引入了一种机制，利用语料库的语义信息为隐式方面构建辅助句子。然后我们鼓励BERT学习响应此辅助句子而非方面本身的特定方面表示。我们在ABSA和Targeted-ABSA任务的真实基准数据集上评估了我们的方法。实验表明，它在所有数据集的方面分类和基于方面的情感方面始终实现最先进的性能，并具有显著的改进幅度。BERT-ASC代码可在https://github.com/amurtadha/BERT-ASC获取。
英文摘要：
Aspect-based sentiment analysis (ABSA) aims to associate a text with a set of aspects and infer their respective sentimental polarities. State-of-the-art approaches are built on fine-tuning pre-trained language models, focusing on learning aspect-specific representations from the corpus. However, aspects are often expressed implicitly, making implicit mapping challenging without sufficient labeled examples, which may be scarce in real-world scenarios. This paper proposes a unified framework to address aspect categorization and aspect-based sentiment subtasks. We introduce a mechanism to construct an auxiliary-sentence for the implicit aspect using the corpus's semantic information. We then encourage BERT to learn aspect-specific representation in response to this auxiliary-sentence, not the aspect itself. We evaluate our approach on real benchmark datasets for both ABSA and Targeted-ABSA tasks. Our experiments show that it consistently achieves state-of-the-art performance in aspect categorization and aspect-based sentiment across all datasets, with considerable improvement margins. The BERT-ASC code is available at https://github.com/amurtadha/BERT-ASC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
