The rate of occurrence of words is not uniform but varies from document to document. Despite this observation, parameters for conventional N-gram language models are usually derived using the assumption of a constant word rate. In this paper we investigate the use of variable word rate assumption, modelled by a Poisson distribution or a continuous mixture of Poissons. We present an approach to estimating the relative frequencies of words or N-grams taking prior information of their occurrences into account. Discounting and smoothing schemes are also considered. Using the Broadcast News task, the approach demonstrates a reduction of perplexity up to 10%.
['Yoshihiko Gotoh', 'Steve Renals']
Variable word rate N-grams