LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Irene Li, Aosong Feng, Hao Wu, Tianxiao Li, Toyotaro Suzumura, Ruihai Dong
Abstract
Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider range of applications in practice. In this paper, we propose a label-interpretable graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph. In this way, we are able to take into account multiple relationships including token-level relationships. Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed. We evaluate our method on four real-world datasets and it achieves competitive scores against selected baseline methods. Specifically, this model achieves a gain of 0.14 on the F1 score in the small label set MLTC, and 0.07 in the large label set scenario.- Anthology ID:
- 2022.dlg4nlp-1.7
- Volume:
- Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Lingfei Wu, Bang Liu, Rada Mihalcea, Jian Pei, Yue Zhang, Yunyao Li
- Venue:
- DLG4NLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 60–70
- Language:
- URL:
- https://aclanthology.org/2022.dlg4nlp-1.7
- DOI:
- 10.18653/v1/2022.dlg4nlp-1.7
- Cite (ACL):
- Irene Li, Aosong Feng, Hao Wu, Tianxiao Li, Toyotaro Suzumura, and Ruihai Dong. 2022. LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification. In Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022), pages 60–70, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification (Li et al., DLG4NLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.dlg4nlp-1.7.pdf
- Data
- RCV1