5th International Conference on Spoken Language Processing
Topic adaptation for language modeling is concerned with adjusting the probabilities in a language model to better reflect the expected frequencies of topical words for a new document. We present a novel technique for adapting a language model to the topic of a document, using a nonlinear interpolation of n-gram language models. A three-way, mutually exclusive division of the vocabulary into general, on-topic and off-topic word classes is used to combine word predictions from a topic-specific and a general language model. We achieve a slight decrease in perplexity and speech recognition word error rate on a Broadcast News test set using these techniques. Our results are compared to results obtained through linear interpolation of topic models.
Bibliographic reference. Seymore, Kristie / Chen, Stanley / Rosenfeld, Ronald (1998): "Nonlinear interpolation of topic models for language model adaptation", In ICSLP-1998, paper 0897.