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

✍ Scribed by Hsi-Jian Lee; Cheng-Huang Tung

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 656 KB
• Volume: 30
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(96)00154-9

✦ Synopsis

Tttis paper presents a new method for clustering the words in a dictionary into word groups. A Chinese character recognition system can then use these groups in a language model to improve the recognition accuracy. In the language model, the number of parameters we must train beforehand can be kept to a reasonable value. The Chinese synonym dictionary Tong2yi4ci2 ci21in2 providing the semantic features is used to calculate 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 word classes are clustered to m groups according to the semantic measurement by a greedy method. The words in the Behavior dictionary can finally be assigned to the m groups. The parameter space for the bigram contextual information of the character recognition system is m 2. From the experimental results, the recognition system with the proposed model has shown better performance than that of a character-based bigram language model ,~) 1997 Pattern Recognition Society.