通过结合中文-BERTology-wwm 和 GCN 来进行中文文本分类
PeerJ. Computer science
|September 14, 2023
概括
本研究介绍了Chinese-BERTology-wwm-GCN,这是一个新的中文文本分类框架,集成了上下文语义和结构信息. 该模型在各种数据集中实现了卓越的性能,即使具有有限的标记数据.
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:
- 文本分类是NLP的一个基本任务.
- 图形神经网络 (GNN) 在具有结构关系的任务中表现出色.
- 大规模的预训练从文本中获取丰富的语义信息.
研究的目的:
- 综合利用语境语义和结构信息来进行中文文本分类.
- 解决现有GNN方法在处理不同长度文本方面的局限性.
- 提出一个有效的框架,将BERT和GCN结合起来,用于中文文本分类.
主要方法:
- 拟议的中文-BERTology-wwm-GCN框架,将来自变压器的中文双向编码器表示 (BERT) 与整个单词掩盖和图形卷积网络 (GCN) 结合起来.
- 用文档和单词作为节点构建了一个异质图形,使用TF-IDF对word-document边缘权重.
- 引入了对长文本的改进点向点相互信息 (PMI*) 和对短文本的辅数相似性,以确定单词边缘权重.
主要成果:
- 拟议的框架在三个中国基准数据集上显著超过了基线方法.
- 即使使用有限的标记训练数据,也实现了强大的性能.
- 证明了语义和结构信息的有效整合,以改进文本分类.
结论:
- 中文-BERTology-wwm-GCN通过利用语义和结构数据为中文文本分类提供了强大的方法.
- 可适应的图形构造方法适用于长短的文本体.
- 该框架对缺乏标记数据的场景显示出希望.
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