@inproceedings{li-etal-2022-ligcn,
title = "{L}i{GCN}: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification",
author = "Li, Irene and
Feng, Aosong and
Wu, Hao and
Li, Tianxiao and
Suzumura, Toyotaro and
Dong, Ruihai",
editor = "Wu, Lingfei and
Liu, Bang and
Mihalcea, Rada and
Pei, Jian and
Zhang, Yue and
Li, Yunyao",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dlg4nlp-1.7",
doi = "10.18653/v1/2022.dlg4nlp-1.7",
pages = "60--70",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
%A Li, Irene
%A Feng, Aosong
%A Wu, Hao
%A Li, Tianxiao
%A Suzumura, Toyotaro
%A Dong, Ruihai
%Y Wu, Lingfei
%Y Liu, Bang
%Y Mihalcea, Rada
%Y Pei, Jian
%Y Zhang, Yue
%Y Li, Yunyao
%S Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F li-etal-2022-ligcn
%X 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.
%R 10.18653/v1/2022.dlg4nlp-1.7
%U https://aclanthology.org/2022.dlg4nlp-1.7
%U https://doi.org/10.18653/v1/2022.dlg4nlp-1.7
%P 60-70
Markdown (Informal)
[LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification](https://aclanthology.org/2022.dlg4nlp-1.7) (Li et al., DLG4NLP 2022)
ACL