Abstract
Aspect-Based Sentiment Analysis (ABSA) is a fundamental task in natural language processing, offering fine-grained insights into opinions expressed in text. While existing research has largely focused on resource-rich languages like English which leveraging large annotated datasets, pre-trained models, and language-specific tools. These resources are often unavailable for low-resource languages such as Bengali. The ABSA task in Bengali remains poorly explored and is further complicated by its unique linguistic characteristics and a lack of annotated data, pre-trained models, and optimized hyperparameters. To address these challenges, this research propose CrosGrpsABS, a novel hybrid framework that leverages bidirectional cross-attention between syntactic and semantic graphs to enhance aspect-level sentiment classification. The CrosGrpsABS combines transformerbased contextual embeddings with graph convolutional networks, built upon rule-based syntactic dependency parsing and semantic similarity computations. By employing bidirectional crossattention, the model effectively fuses local syntactic structure with global semantic context, resulting in improved sentiment classification performance across both low- and high-resource settings. We evaluate CrosGrpsABS on four low-resource Bengali ABSA datasets and the high-resource English SemEval 2014 Task 4 dataset. The CrosGrpsABS consistently outperforms existing approaches, achieving notable improvements, including a 0.93% F1-score increase for the Restaurant domain and a 1.06% gain for the Laptop domain in the SemEval 2014 Task 4 benchmark.
Abstract (translated)
基于方面的情感分析（ABSA）是自然语言处理中的一个基本任务，它为文本中表达的意见提供了细致的洞察。尽管现有研究主要集中在资源丰富的语言上，如英语，并且依赖于大规模标注数据集、预训练模型和特定的语言工具，这些资源对于像孟加拉语这样的低资源语言往往是不可用的。孟加拉语中的ABSA任务尚未充分探索，并且由于其独特的语言特征以及缺乏标注数据、预训练模型和优化的超参数而变得更加复杂。 为了应对这些挑战，这项研究提出了CrosGrpsABS，这是一种新颖的混合框架，利用句法图与语义图之间的双向交叉注意来增强方面级情感分类。CrosGrpsABS结合了基于转换器的上下文嵌入和图形卷积网络，并建立在基于规则的句法依存分析和语义相似性计算之上。通过使用双向交叉注意力机制，该模型有效地融合了局部句法结构与全局语义背景信息，在低资源环境和高资源环境中均提高了情感分类性能。 我们在四个孟加拉语低资源ABSA数据集以及高资源英语SemEval 2014 Task 4数据集上对CrosGrpsABS进行了评估。在所有评测中，CrosGrpsABS始终优于现有方法，并实现了显著的改进，包括在SemEval 2014 Task 4基准测试中的餐厅领域和笔记本电脑领域的F1得分分别提高了0.93%和1.06%。
URL
https://arxiv.org/abs/2505.19018
