# Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling

arXiv: https://arxiv.org/abs/2112.09488 · HTML: https://mlsanity.com/paper/2112.09488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Fast and Accurate Neural CRF Constituency Parsing](https://mlsanity.com/paper/2008.03736/) (2020)
- [ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations](https://mlsanity.com/paper/1911.00720/) (2019)
- [Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF](https://mlsanity.com/paper/1704.01314/) (2017)
- [Hybrid semi-Markov CRF for Neural Sequence Labeling](https://mlsanity.com/paper/1805.03838/) (2018)
- [Span Labeling Approach for Vietnamese and Chinese Word Segmentation](https://mlsanity.com/paper/2110.00156/) (2021)

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

- [Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN](https://mlsanity.com/paper/2306.02078/) (2023)
- [An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling](https://mlsanity.com/paper/2402.13534/) (2024)
- [AMNet: An Acoustic Model Network for Enhanced Mandarin Speech Synthesis](https://mlsanity.com/paper/2504.09225/) (2025)

## Abstract (arXiv)

Chinese word segmentation and part-of-speech tagging are necessary tasks in terms of computational linguistics and application of natural language processing. Many re-searchers still debate the demand for Chinese word segmentation and part-of-speech tagging in the deep learning era. Nevertheless, resolving ambiguities and detecting unknown words are challenging problems in this field. Previous studies on joint Chinese word segmentation and part-of-speech tagging mainly follow the character-based tagging model focusing on modeling n-gram features. Unlike previous works, we propose a neural model named SpanSegTag for joint Chinese word segmentation and part-of-speech tagging following the span labeling in which the probability of each n-gram being the word and the part-of-speech tag is the main problem. We use the biaffine operation over the left and right boundary representations of consecutive characters to model the n-grams. Our experiments show that our BERT-based model SpanSegTag achieved competitive performances on the CTB5, CTB6, and UD, or significant improvements on CTB7 and CTB9 benchmark datasets compared with the current state-of-the-art method using BERT or ZEN encoders.