A probabilistic framework for Chinese spelling check
Journal
ACM Transactions on Asian and Low-Resource Language Information Processing
Journal Volume
14
Journal Issue
4
Date Issued
2015
Author(s)
Abstract
Chinese spelling check (CSC) is still an unsolved problem today since there are many homonymous or homomorphous characters. Recently, more and more CSC systems have been proposed. To the best of our knowledge, language modeling is one of the major components among these systems because of its simplicity and moderately good predictive power. After deeply analyzing the school of research, we are aware that most of the systems only employ the conventional n-gram language models. The contributions of this article are threefold. First, we propose a novel probabilistic framework for CSC, which naturally combines several important components, such as the substitution model and the language model, to inherit their individual merits as well as to overcome their limitations. Second, we incorporate the topic language models into the CSC system in an unsupervised fashion. The topic language models can capture the long-span semantic information from a word (character) string while the conventional n-gram language models can only preserve the local regularity information. Third, we further integrate Web resources with the proposed framework to enhance the overall performance. Our rigorously empirical experiments demonstrate the consistent and utility performance of the proposed framework in the CSC task. ? 2015 ACM.
Subjects
Chinese
Language model
Probabilistic
Spelling check
Topic modeling
SDGs
Type
conference paper