# Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN

arXiv: https://arxiv.org/abs/2306.02078 · HTML: https://mlsanity.com/paper/2306.02078/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 3 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)
- [Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF](https://mlsanity.com/paper/1704.01314/) (2017)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [RethinkCWS: Is Chinese Word Segmentation a Solved Task?](https://mlsanity.com/paper/2011.06858/) (2020)
- [Switch-LSTMs for Multi-Criteria Chinese Word Segmentation](https://mlsanity.com/paper/1812.08033/) (2018)
- [A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging](https://mlsanity.com/paper/1611.05384/) (2016)
- [Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling](https://mlsanity.com/paper/2112.09488/) (2021)
- [Chinese Word Segmentation with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2201.08975/) (2022)

## Built on by (most cited first, 1 of 1)

- [Towards Computational Chinese Paleography](https://mlsanity.com/paper/2601.06753/) (2026)

## Abstract (arXiv)

Recently, it is quite common to integrate Chinese sequence labeling results to enhance syntactic and semantic parsing. However, little attention has been paid to the utility of hierarchy and structure information encoded in syntactic and semantic features for Chinese sequence labeling tasks. In this paper, we propose a novel framework to encode syntactic structure features and semantic information for Chinese sequence labeling tasks with graph convolutional networks (GCN). Experiments on five benchmark datasets, including Chinese word segmentation and part-of-speech tagging, demonstrate that our model can effectively improve the performance of Chinese labeling tasks.