FINITE STATE LANGUAGE MODELS SMOOTHED USING n-GRAMS
David Llorens, Juan Miguel Vilar, Francisco Casacuberta · International Journal of Pattern Recognition and Artificial Intelligence · 2002
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.