Language Model Based on Word Clustering

摘要

Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and not large amount of computation. (2) class-based method always loses some prediction ability to adapt the text of different domain. The authors try to solve above problems in this paper. This paper presents a definition of word similarity by utilizing mutual information. Based on word similarity, this paper gives the definition of word set similarity. Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance. At the same time, this paper presents a new method to create the vari-gram model.