MLGN:A Multi-Label Guided Network for Improving Text Classification

Author:

Liu Qiang1ORCID,Chen Jingzhe2,Chen Fan1ORCID,Fang Kejie1,An Peng3,Zhang Yiming4,Du Shiyu4

Affiliation:

1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China

2. Zhejiang Tianyan Technology Company Ltd., Hangzhou, China

3. College of Electronics and Information Engineering, Ningbo University of Technology, Ningbo, China

4. Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China

Funder

National Natural Science Foundation of China

Key Research and Development Projects of Zhejiang Province

Zhejiang Province Key Research and Development Program

Project of the Key Technology for Virtue Reactors from NPIC Entrepreneurship Program of Foshan National Hi-Tech Industrial Development Zone

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference35 articles.

1. AttentionXML: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification;you;Proc Adv Neural Inf Process Syst,2019

2. Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain

3. Recurrent Convolutional Neural Networks for Text Classification

4. Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification

5. BERT: Pre-training of deep bidirectional transformers for language understanding;devlin;arXiv 1810 04805,2018

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