跨语言基于方面的情感分析：关于任务、方法和挑战的综述
Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges
AI总结：
本文全面综述了跨语言基于方面的情感分析（ABSA）的任务、数据集、建模与迁移方法，并探讨了基于LLMs的研究现状及未来挑战。
摘要：
基于方面的情感分析（ABSA）是一项细粒度的情感分析任务，专注于理解方面级别的意见，包括对特定方面术语、类别和观点的情感。尽管ABSA研究取得了显著进展，但大部分研究集中在单语环境上。跨语言ABSA旨在将知识从资源丰富的语言（如英语）迁移到低资源语言，目前仍是一个探索不足的领域，且缺乏对该领域的系统综述。本文旨在通过提供跨语言ABSA的全面综述来填补这一空白。我们总结了关键的ABSA任务，包括方面术语提取、方面情感分类以及涉及多个情感元素的复合任务。此外，我们回顾了用于解决这些任务的数据集、建模范式和跨语言迁移方法。我们还考察了单语和多语言ABSA以及结合LLMs的ABSA现有工作如何促进跨语言ABSA的发展。最后，我们强调了主要挑战，并为推进跨语言ABSA系统提出了未来研究方向。
英文摘要：
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that focuses on understanding opinions at the aspect level, including sentiment towards specific aspect terms, categories, and opinions. While ABSA research has seen significant progress, much of the focus has been on monolingual settings. Cross-lingual ABSA, which aims to transfer knowledge from resource-rich languages (such as English) to low-resource languages, remains an under-explored area, with no systematic review of the field. This paper aims to fill that gap by providing a comprehensive survey of cross-lingual ABSA. We summarize key ABSA tasks, including aspect term extraction, aspect sentiment classification, and compound tasks involving multiple sentiment elements. Additionally, we review the datasets, modelling paradigms, and cross-lingual transfer methods used to solve these tasks. We also examine how existing work in monolingual and multilingual ABSA, as well as ABSA with LLMs, contributes to the development of cross-lingual ABSA. Finally, we highlight the main challenges and suggest directions for future research to advance cross-lingual ABSA systems.
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。
