Distribution-based Pruning of Backoff Language Models - Gao, Jianfeng; Lee, Kai-Fu (Association for Computational Linguistics, 2000)
We propose a distribution-based pruning of n-gram backoff language models. Instead of the conventional approach of pruning n-grams that are infrequent in training data, we prune n-grams that are likely to be infrequent in a new document. Our method is based on the n-gram distribution i.e. the probability that an n-gram occurs in a new document. Experimental results show that our method performed 7-9% (word perplexity reduction) better than conventional cutoff methods.