tRF-BERT:在孟加拉语中,基于方面的情绪分析的变革性方法
Shihab Ahmed1, Moythry Manir Samia1, Maksuda Haider Sayma2
1Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh.
PloS one
|September 20, 2024
概括
这项研究通过使用tRF-BERT等先进模型,增强了孟加拉语的基于方面的情感分析. 我们的新方法显著提高了基准数据集的准确性和F1得分.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 情感分析越来越重要,特别是在孟加拉文本.
- 由于数据稀缺和语言复杂性,孟加拉语的基于方面情感分析 (ABSA) 是一个挑战.
研究的目的:
- 为了提高孟加拉语的基于方面的情感分析 (ABSA) 性能.
- 评估基于变压器的模型和孟加拉语ABSA的新型混合模型的有效性.
主要方法:
- 使用了两个孟加拉语基准数据集:板球和餐厅.
- 实施并比较了来自变压器的双向编码器表示 (BERT),强大优化的BERT方法 (RoBERTa) 和拟议的变革性随机森林和BERT (tRF-BERT) 混合模型.
- 与现有的随机森林,支持向量机,K-最近邻居和卷积神经网络模型进行基准测试.
主要成果:
- 所有实施的模型都超过了对基准数据集的先前工作.
- 拟议的tRF-BERT模型实现了最高的精度和F1分数.
- 在Cricket数据集上实现了0.89准确度和0.85F1分数的方面检测.
- 在餐厅数据集上实现了0.92准确度和0.89F1分数的方面检测.
结论:
- 这种tRF-BERT混合模型为孟加拉语面向基于情感分析提供了一种优越的方法.
- 基于变压器的模型,特别是拟议的混合型,在处理孟加拉语NLP任务方面取得了重大进展.
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