A language model based on semantically clustered words in a Chinese character recognition system

This paper presents a new method for clustering the words in a dictionary into word groups, which are applied in a Chinese character recognition system with a language model to describe the contextual information. The Chinese synonym dictionary Tong2yi4ci2 ci2lin2 providing the semantic features is used to train the weights of the semantic attributes of the character-based word classes. The weights of the semantic attributes are next updated according to the words of the behavior dictionary, which has a rather complete word set. Then, the updated word classes are clustered into m groups according to the semantic measurement by a greedy method. The words in the behavior dictionary can finally be assigned into the m groups. The parameter space for bigram contextual information of the character recognition system is m². From the experimental results, the recognition system with the proposed model has shown better performance than that of a character-based bigram language model