# Chinese Word Segmentation with Heterogeneous Graph Neural Network

arXiv: https://arxiv.org/abs/2201.08975 · HTML: https://mlsanity.com/paper/2201.08975/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## 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 on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://mlsanity.com/paper/1704.04675/) (2017)
- [Text Level Graph Neural Network for Text Classification](https://mlsanity.com/paper/1910.02356/) (2019)
- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF](https://mlsanity.com/paper/1704.01314/) (2017)
- [Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning](https://mlsanity.com/paper/1903.04190/) (2019)
- [Neural Word Segmentation with Rich Pretraining](https://mlsanity.com/paper/1704.08960/) (2017)
- [State-of-the-art Chinese Word Segmentation with Bi-LSTMs](https://mlsanity.com/paper/1808.06511/) (2018)
- [Adversarial Multi-Criteria Learning for Chinese Word Segmentation](https://mlsanity.com/paper/1704.07556/) (2017)
- [Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation](https://mlsanity.com/paper/2007.08186/) (2020)
- [Neural Chinese Word Segmentation with Dictionary Knowledge](https://mlsanity.com/paper/1807.05849/) (2018)
- [Effective Neural Solution for Multi-Criteria Word Segmentation](https://mlsanity.com/paper/1712.02856/) (2017)
- [Improving Cross-Domain Chinese Word Segmentation with Word Embeddings](https://mlsanity.com/paper/1903.01698/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN](https://mlsanity.com/paper/2306.02078/) (2023)

## Abstract (arXiv)

In recent years, deep learning has achieved significant success in the Chinese word segmentation (CWS) task. Most of these methods improve the performance of CWS by leveraging external information, e.g., words, sub-words, syntax. However, existing approaches fail to effectively integrate the multi-level linguistic information and also ignore the structural feature of the external information. Therefore, in this paper, we proposed a framework to improve CWS, named HGNSeg. It exploits multi-level external information sufficiently with the pre-trained language model and heterogeneous graph neural network. The experimental results on six benchmark datasets (e.g., Bakeoff 2005, Bakeoff 2008) validate that our approach can effectively improve the performance of Chinese word segmentation. Importantly, in cross-domain scenarios, our method also shows a strong ability to alleviate the out-of-vocabulary (OOV) problem.