Simple recurrent network for Chinese word prediction

This paper presents preliminary investigations concerning the use of simple recurrent network (SRN) in Chinese word prediction. We explore the architecture introduced by Elman (1990) for predicting successive elements of a sequence. This model is based on a multilayer architecture and contains special units, called context units which provide the short-term memory (STM) in the system. Based on this model, We constructed a modular SRN to predict Chinese word at two levels. The first level network predicts the major category of the next word, then the next possible word is predicted at the second level network. Also, the specific encoding schemes was described in the paper. Experiments show that the method is promising.