Label-Specific Document Representation for Multi-Label Text Classification

Lin Xiao, Xin Huang, Boli Chen, Liping Jing


Abstract
Multi-label text classification (MLTC) aims to tag most relevant labels for the given document. In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. LSAN takes advantage of label semantic information to determine the semantic connection between labels and document for constructing label-specific document representation. Meanwhile, the self-attention mechanism is adopted to identify the label-specific document representation from document content information. In order to seamlessly integrate the above two parts, an adaptive fusion strategy is proposed, which can effectively output the comprehensive label-specific document representation to build multi-label text classifier. Extensive experimental results demonstrate that LSAN consistently outperforms the state-of-the-art methods on four different datasets, especially on the prediction of low-frequency labels. The code and hyper-parameter settings are released to facilitate other researchers.
Anthology ID:
D19-1044
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
466–475
Language:
URL:
https://aclanthology.org/D19-1044
DOI:
10.18653/v1/D19-1044
Bibkey:
Cite (ACL):
Lin Xiao, Xin Huang, Boli Chen, and Liping Jing. 2019. Label-Specific Document Representation for Multi-Label Text Classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 466–475, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Label-Specific Document Representation for Multi-Label Text Classification (Xiao et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1044.pdf
Code
 EMNLP2019LSAN/LSAN
Data
20000 utterances