A Survey of Multi-label Text Classification Based on Deep Learning

被引：6
|
作者：
Chen, Xiaolong [1 ]
Cheng, Jieren [1 ,2 ]
Liu, Jingxin [1 ]
Xu, Wenghang [3 ]
Hua, Shuai [1 ]
Tang, Zhu [1 ]
Sheng, Victor S. [4 ]
机构：
[1] Hainan Univ, Sch Comp Sci & Technol, Haikou 570228, Peoples R China
[2] Hainan Univ, Hainan Blockchain Technol Engn Res Ctr, Haikou 570228, Peoples R China
[3] Hainan Univ, Sch Cyberspace Secur, Haikou 570228, Peoples R China
[4] Texas Tech Univ, Dept Comp Sci, Lubbock, TX 79409 USA
来源：
ARTIFICIAL INTELLIGENCE AND SECURITY, ICAIS 2022, PT I | 2022年 / 13338卷
基金：
中国国家自然科学基金;
关键词：
Natural Language Processing; Text classification; Multi-label; Deep learning; QUESTION ANSWERING SYSTEMS; ATTENTION; NETWORKS;
D O I：
10.1007/978-3-031-06794-5_36
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Text classification (TC) is an important basic task in the field of Natural Language Processing (NLP), and multi-label text classification (MLTC) is an important branch of TC. MLTC has undergone a transformation from traditional machine learning to deep learning, and various models with excellent performance have emerged one after another. But at present, the focus of various related researches is also varied, so we combed the excellent research results in the field of MLTC in recent years, and classified them according to the focus of their research. At the same time, we also summarized the relevant data sets and evaluation indicators in the field of multi-label text classification, and made a prospect for the future of the field of MLTC.
引用
收藏
页码：443 / 456
页数：14
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