Distilroberta2gnn:一种新的混合深度学习方法,用于基于方面的情绪分析
Aseel Alhadlaq1, Alaa Altheneyan1
1Department of Computer Science and Engineering, College of Applied Studies and Community Service, King Saud University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|September 24, 2024
概括
这项研究介绍了Distil-RoBERTa2GNN,这是一种基于方面的情绪分析 (ABSA) 的新型混合模型. 该模型在基准数据集上实现了竞争性表现,解决了细微的语言解释和数据稀缺方面的挑战.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 基于方面的情感分析 (ABSA) 对于理解文本中关于特定主题的意见至关重要.
- 目前的ABSA方法与细微的语言细微差别以及缺乏高质量的,特定于领域的数据集作斗争.
- 现有的模型往往缺乏适应不同语言环境所需的适应性.
研究的目的:
- 引入和评估Distil-RoBERTa2GNN模型,这是ABSA的一种新的混合方法.
- 解决解释细微语言的挑战,以及ABSA中注释数据集的稀缺性.
- 为ABSA研究建立一个新的绩效基准.
主要方法:
- 开发了一种混合模型,将DistilRoBERTa用于特征提取和图形神经网络 (GNN) 用于分类.
- 实施了全面的四阶段数据预处理策略,以提高培训数据质量.
- 在四个基准数据集上评估模型:Rest14,Rest15,Rest16-EN和Rest16-ESP.
主要成果:
- 在数据集中获得了强大的F1分数:Rest14 (77.98%),Rest15 (76.86%),Rest16-EN (84.96%) 和Rest16-ESP (74.87%).
- 与ABSA中的各种基线模型相比,证明了竞争性表现.
- 突出了该模型在处理特定领域情绪分析任务中的有效性.
结论:
- 蒸-RoBERTa2GNN模型为基于方面的情绪分析提供了一个强大的解决方案.
- 混合方法有效地利用预先训练的语言模型和GNN来改进情绪分类.
- 这项研究通过增强不同数据集的模型适应性和性能来促进ABSA的进步.
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