mlsanityData to 7 Oct 2026

LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

arXiv 2103.14620 ↗First posted 26 Mar 2021cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 26 Mar 2021.

Builds on

20 of 38 references.

References · 20

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8 more
  1. 2020HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classificationcited by 21
  2. 2021Does Head Label Help for Long-Tailed Multi-Label Text Classificationcited by 13
  3. 2020EmoGraph: Capturing Emotion Correlations using Graph Networkscited by 11
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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1.

3 papers.

  1. 2022Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequencescited by 12
  2. 2022An Effective Deployment of Contrastive Learning in Multi-label Text Classificationcited by 9
  3. 2025LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Rerankingcited by 1