Improving Trigram Language Modeling with the World Wide Web

We propose a novel method for using the World Wide Web to acquire trigram estimates for statistical lan-

guage modeling. We submit an N-gram as a phrase query to web search engines. The search engines return

the number of web pages containing the phrase, from which the N-gram count is estimated. The N-gram

counts are then used to form web-based trigram probability estimates. We discuss the properties of such

estimates, and methods to interpolate them with traditional corpus based trigram estimates. We show that

the interpolated models improve speech recognition word error rate significantly over a small test set.