A WORD VECTOR BASED QUANTIZATION MODEL OF CHINESE WORD SENSE

A word vector based Chinese word sense quantization model is proposed, which can be used to solve problems such as auto acquisition and quantization of word sense information. The modeling method of the model and its applications in the Chinese word sense disambiguation are further described. And then, the model’s ability to discriminate word sense is evaluated by constructing pseudoword. The experiment shows that this model has a good representation for word sense. As the construction of model is done via the statistic of large scale rough corpora, huge workload of manually quantization word senses is avoided. So this model can be applied to many word sense related NLP tasks.