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

arXiv: https://arxiv.org/abs/2103.14620 · HTML: https://mlsanity.com/paper/2103.14620/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://mlsanity.com/paper/1704.04675/) (2017)
- [Deep Extreme Multi-label Learning](https://mlsanity.com/paper/1704.03718/) (2017)
- [NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning](https://mlsanity.com/paper/1804.06658/) (2018)
- [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](https://mlsanity.com/paper/1811.12181/) (2018)
- [HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2008.12842/) (2020)
- [Does Head Label Help for Long-Tailed Multi-Label Text Classification](https://mlsanity.com/paper/2101.09704/) (2021)
- [EmoGraph: Capturing Emotion Correlations using Graph Networks](https://mlsanity.com/paper/2008.09378/) (2020)
- [MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks](https://mlsanity.com/paper/1811.08800/) (2018)
- [A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation](https://mlsanity.com/paper/1910.14076/) (2019)
- [R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning](https://mlsanity.com/paper/2004.10610/) (2020)
- [What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing](https://mlsanity.com/paper/2004.10899/) (2020)
- [Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation](https://mlsanity.com/paper/1911.02147/) (2019)

## Built on by (most cited first, 3 of 3)

- [Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences](https://mlsanity.com/paper/2210.11794/) (2022)
- [An Effective Deployment of Contrastive Learning in Multi-label Text Classification](https://mlsanity.com/paper/2212.00552/) (2022)
- [LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Reranking](https://mlsanity.com/paper/2503.07968/) (2025)

## 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.