A Hybrid Chinese Spelling Correction Using Language Model and Statistical Machine Translation with Reranking

Abstract

We describe the Nara Institute of Science and Technology (NAIST) spelling check system in the shared task. Our system contains three components: a word segmentation based language model to generate correction candidates; a statistical machine translation model to provide correction candidates and a Support Vector Machine (SVM) classifier to rerank the candidates provided by the previous two components. The experimental results show that the kbest language model and the statistical machine translation model could generate almost all the correction candidates, while the precision is very low. However, using the SVM classifier to rerank the candidates, we could obtain higher precision with a little recall dropping. To address the low resource problem of the Chinese spelling check, we generate 2 million artificial training data by simply replacing the character in the provided training sentence with the character in the confusion set.