计算机工程与应用2024，Vol.60Issue(4)：163-172,10.DOI:10.3778/j.issn.1002-8331.2209-0472
采用平衡函数的大规模多标签文本分类
Extreme Multi-Label Text Classification Based on Balance Function
摘要
Abstract
Extreme multi-label text classification is a challenging task in the field of natural language processing.In this task,there is a long-tailed distribution situation of labeled data.In this situation,model has a poor ability to learn tail labels classification,which results the overall classification effect is not good.In order to address the above problems,an extreme multi-label text classification method based on balance function is proposed.Firstly,the BERT pre-training model is used for word embedding.Further,the concatenated output of the multi-layer encoder in the pre-trained model is used as the text vector representation to obtain richer text semantic information and improves the model convergence speed.Finally,the balance function is used to assign different attenuation weights to the training losses of different prediction labels,which improves the learning ability of the method on tail label classification.The experimental results on Eurlex-4K and Wiki10-31K datasets show that the evaluation indicators P@1,P@3 and P@5 respectively reach 86.95%,74.12%,61.43%and 88.57%,77.46%and 67.90%.关键词
自然语言处理/大规模多标签文本分类/BERT/平衡函数/深度学习Key words
natural language processing(NLP)/extreme multi-label text classification/BERT/balance function/deep learning分类
信息技术与安全科学引用本文复制引用
陈钊鸿,洪智勇,余文华,张昕..采用平衡函数的大规模多标签文本分类[J].计算机工程与应用,2024,60(4):163-172,10.基金项目
五邑大学港澳联合研发基金(2019WGALH21) （2019WGALH21）
广东省基础与应用基础研究基金(2020A1515011468) （2020A1515011468）
广东省普通高校特色创新类项目(2019KTSCX189). （2019KTSCX189）
