在多域数据集上使用基于转换的方法对泰鲁古语的情绪分类
Kannaiah Chattu1, K Adi Narayana Reddy2, Sai Babu Veesam3
1Department of Computer Science & Engineering (AIML), Malla Reddy College of Engineering & Technology, Maisammaguda, Bhadurpalle, Hyderabad, 500100, Telangana, India.
Scientific reports
|July 2, 2025
概括
这项研究评估了四种变压器模型,用于分析特鲁古语的情绪,这是一种资源较低的语言. XLM-RoBERTa实现了79.42%的准确性,为泰语自然语言处理 (NLP) 建立了一个基准.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 对资源丰富的语言而言,情感分析至关重要,但由于数据稀缺,对泰语等资源较低的语言来说,其发展不足.
- 基于变压器的模型对NLP任务有前途,对低资源语言的多语言预训练模型越来越感兴趣.
研究的目的:
- 评估四个预训练过的变压器模型 (IndicBERT,RoBERTa,DeBERTa,XLM-RoBERTa) 在泰鲁古语的句子级情绪分析中的有效性.
- 为了建立一个可靠的标杆,以分析泰鲁古人的情绪.
主要方法:
- 评估了四种变压器型号:IndicBERT,RoBERTa,DeBERTa和XLM-RoBERTa. 这四种变压器型号包括:
- 利用一个自定义的数据集",Sentikanna",包括泰鲁古语的多种域数据集.
- 在三个不同的数据集中比较模型性能,以进行可靠的评估.
主要成果:
- XLM-RoBERTa表现出强的表现,在二进制情绪分类中获得了79.42%的准确性.
- 这四种模型都显示出有希望的结果,表明以变压器为基础的方法对泰国语情绪分析的可行性.
结论:
- 基于变压器的模型对于泰鲁古语情绪分析是有效的,即使资源有限.
- XLM-RoBERTa为泰语NLP的未来研究和开发提供了强有力的基础.
- "Sentikanna"数据集是评估泰鲁古语情绪分析模型的宝贵资源.
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