# Improving Cross-Domain Chinese Word Segmentation with Word Embeddings

arXiv: https://arxiv.org/abs/1903.01698 · HTML: https://mlsanity.com/paper/1903.01698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

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

- [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)

## Abstract (arXiv)

Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our code and data available on Github.