A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check

Dingmin Wang, Yan Song, Jing Li, Jialong Han, Haisong Zhang
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing (NLP) applications, but also facilitates reading and understanding of running texts in peoples' daily lives. However, to utilize datadriven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with
more » ... ally generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCRand ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach. * This work was conducted during Dingmin Wang's internship in Tencent AI Lab.
doi:10.18653/v1/d18-1273 dblp:conf/emnlp/WangSLHZ18 fatcat:2oqyi2fleff5lftjig4xvpedgy