多语言文本分类和情绪分析:对使用多语言方法对推特数据进行分类的比较分析
George Manias1, Argyro Mavrogiorgou1, Athanasios Kiourtis1
1University of Piraeus, Piraeus, Greece.
Neural computing & applications
|June 26, 2023
概括
这项研究比较了多语言BERT和零射击模型,用于对社交媒体数据的文本和情绪分析. 微调的BERT模型提供了更高的准确性,而零射击方法为多语言文本分类提供了更快,更可扩展的解决方案.
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
- 社交媒体分析
背景情况:
- 社交媒体产生了大量的多语言用户生成的文本数据.
- 分析这些数据进行文本分类和情绪分析是具有挑战性的,因为多样性和多语言.
- 需要无主体的多语言NLP解决方案.
研究的目的:
- 对文本和情感分类的多语言方法进行比较分析.
- 评估不同模型在多语言语料库上的准确性和适用性.
- 调查不同多语言NLP策略之间的权衡.
主要方法:
- 使用了四个基于BERT的多语言分类器.
- 使用零射击分类方法.
- 在多语言文本库上对情绪和文本分类进行模型性能比较.
主要成果:
- 基于BERT的多语言分类器在对多语言数据进行微调时显示出高性能和推理传输.
- 零射击方法为多语言解决方案提供了一种更快,更有效,更可扩展的方法,可以适应新语言和新任务.
- 零射击模型在许多语言中取得了良好的结果,但通常不如微调的BERT模型准确.
结论:
- 当准确性至关重要时,微调的多语言BERT模型是优越的.
- 零射击分类为多语言NLP任务提供了一个可扩展和适应的替代方案,特别是当速度是一个因素时.
- 这两种方法都为处理多语言用户生成内容的复杂性提供了有价值的策略.
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