Combining Neural Networks and Statistics for Chinese Word Sense Discrimination

The input of network is the key problem for Chinese word sense discrimination utilizing the neural network. This paper presents an input model of neural network that calculates the mutual information between contextual words and ambiguous word by using statistical method and taking the contextual words to certain number beside the ambiguous word according to (-M, +N). The experiment adopts triple-layer BP neural network model and proves how the size of training set and the value of M and N affect the performance of neural network model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. Tested accuracy of our approach on a closed-corpus reaches 90.31%, and 89.62% on a open-corpus. The experiment proves that the neural network model has good performance on word sense Discrimination.