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Abstract

David Llorens, Juan M. Vilar, Francisco Casacuberta. Finite state language models smoothed using n-grams. International Journal of Pattern Recognition and Artificial Intelligence, 2002. Vol. 16 (3), pp. 275-289.

We address the problem of smoothing the probability distribution defined by a finite state automaton. Our approach extends the ideas employed for smoothing n-gram models. This extension is obtained by interpreting n-gram models as finite state models. The experiments show that our smoothing improves perplexity over smoothed n-grams and Error Correcting Parsing techniques.