A real-valued vector representation of Chinese words based on neural language model is called distributed representation of words

and the corresponding word feature is also called distributed word feature. The experiments used distributed word feature replacing traditional word feature for the identification of Chinese Base Chunks were carried out based on the conditional random field model on the TCT corpus of Tsinghua University.The results show that the marking precision using distributed word features improves 38.01% than the traditional word feature model only using sliding-window word features of size[-2

2] and 1.86% than using sliding-window word features of size[-2

2]+part-of-speech feature

respectively. Thisindicates that the distributed representation of Chinese words is available for the identification task of Chinese base chunk.