A Hybrid Approach to Adaptive Statistical Language Modeling
Ronald Rosenfeld · 1994
We desert'be our latest attempt at adaptive language modeling.At the heart of our approach is a Maximum Entropy (ME) model which inc.orlxnates many knowledge sources in a consistent manner.The other components are a selective unigram cache, a conditional bigram cache, and a conventionalstatic trigram.We describe the knowledge sources used to build such a model with ARPA's official WSJ corpus, and report on perplexity and word error rate results obtained with it.Then, three different adaptation paradigms are discussed, and an additional experiment, based on AP wire data, is used to compare them.