Chinese Word Segmentation: Another Decade Review (2007-2017)
Computation and Language
2019-01-21 v1
Abstract
This paper reviews the development of Chinese word segmentation (CWS) in the most recent decade, 2007-2017. Special attention was paid to the deep learning technologies that has already permeated into most areas of natural language processing (NLP). The basic view we have arrived at is that compared to traditional supervised learning methods, neural network based methods have not shown any superior performance. The most critical challenge still lies on balancing of recognition of in-vocabulary (IV) and out-of-vocabulary (OOV) words. However, as neural models have potentials to capture the essential linguistic structure of natural language, we are optimistic about significant progresses may arrive in the near future.
Cite
@article{arxiv.1901.06079,
title = {Chinese Word Segmentation: Another Decade Review (2007-2017)},
author = {Hai Zhao and Deng Cai and Changning Huang and Chunyu Kit},
journal= {arXiv preprint arXiv:1901.06079},
year = {2019}
}
Comments
in Chinese