An unsupervised language model adaptation based on keyword clustering and query availability estimation

Abstract

Language model adaptation using text data downloaded from the WWW is an efficient way to train a topic-specific LM. We are developing an unsupervised LM adaptation method using data in the Web. The one key point of unsupervised Web-based LM adaptation is how to select keywords to compose the search query. In this paper, we propose a new method of selecting keywords from keyword candidates, which uses a keyword clustering technique based on word similarities. The other key point is how to determine the number of downloaded pages for each query. In this paper we propose a method to estimate "a query availability," which is based on a small number of downloaded Web pages. The experimental result showed that the determination of downloaded pages using the query availability was effective than the conventional methods that determined the number of pages empirically.