Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kamran Aziz , Naveed Ahmed , Hassan Jalil Hadi , Aizihaierjiang Yusufu , Mohammaed Ali Alshara , Yasir Javed , Donghong Ji
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Abstract

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.
增强型 UrduAspectNet:利用双峰注意力实现卓越的基于方面的情感分析
乌尔都语具有丰富的语言复杂性,给计算情感分析带来了巨大挑战。本研究介绍了 UrduAspectNet 的增强版,该版本专为基于方面的乌尔都语情感分析 (ABSA) 而设计。我们引入了一些关键的创新,包括在模型架构中加入 Biaffine Attention,使 XLM-R 嵌入、双向 LSTM(BiLSTM)和双图卷积网络(GCN)协同增效。此外,我们还利用依赖性解析为 GCNs 创建邻接矩阵,捕捉句法依赖性以增强关系表示。改进后的模型被称为 "增强型 UrduAspectNet",它将通过 BiLSTM 和 GCN 层处理的 POS 和词素嵌入与 Biaffine Attention 整合在一起,从而增强了对错综复杂的方面和情感关系的提取。我们还引入了 BIO 标签用于方面术语识别,从而提高了方面提取的粒度。实验结果表明,方面提取和情感分类的准确性都有显著提高。这项研究推动了乌尔都语情感分析的发展,为在代表性不足的语言中利用复杂的 NLP 技术开创了先例。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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