Traditional approaches to language modelling have relied on a fixed corpus of text to inform the parameters of a probability distribution over word sequences. Increasing the corpus size often leads to better-performing language models, but no matter how large, the corpus is a static entity, unable to reflect information about events which postdate it. In these pages we introduce an online paradigm which interleaves the estimation and application of a language model. We present a Bayesian approach to online language modelling, in which the marginal probabilities of a static trigram model are dynamically updated to match the topic being dictated to the system. We also describe the architecture of a prototype we have implemented which uses the World Wide Web (WWW) as a source of information, and provide results from some initial proof of concept experiments. 1. BACKGROUND Of pressing concern to language modelling researchers is how to detect and account for a "non-stationary" source; tha...

Traditional approaches to language modelling have relied on a fixed corpus of text to inform the parameters of a probability distribution over word sequences. Increasing the corpus size often leads to better-performing language models, but no matter how large, the corpus is a static entity, unable to reflect information about events which postdate it. In these pages we introduce an online paradigm which interleaves the estimation and application of a language model. We present a Bayesian approach to online language modelling, in which the marginal probabilities of a static trigram model are dynamically updated to match the topic being dictated to the system. We also describe the architecture of a prototype we have implemented which uses the World Wide Web (WWW) as a source of information, and provide results from some initial proof of concept experiments. 1. BACKGROUND Of pressing concern to language modelling researchers is how to detect and account for a "non-stationary" source; th...