# Neural Chinese Word Segmentation with Dictionary Knowledge

arXiv: https://arxiv.org/abs/1807.05849 · HTML: https://mlsanity.com/paper/1807.05849/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory](https://mlsanity.com/paper/2209.02967/) (2022)
- [Chinese Word Segmentation with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2201.08975/) (2022)
- [A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis](https://mlsanity.com/paper/2101.11423/) (2021)
- [Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application](https://mlsanity.com/paper/2111.09078/) (2021)

## Abstract (arXiv)

Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large number of labeled sentences for model training, and usually cannot utilize the useful information in Chinese dictionary. In this paper, we propose two methods to exploit the dictionary information for CWS. The first one is based on pseudo labeled data generation, and the second one is based on multi-task learning. The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.