Accession Number:

ADA458711

Title:

A Hybrid Approach to Adaptive Statistical Language Modeling

Descriptive Note:

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

1994-01-01

Pagination or Media Count:

7.0

Abstract:

We describe our latest attempt at adaptive language modeling. At the heart of our approach is a Maximum Entropy ME model, which incorporates many knowledge sources In a consistent manner. The other components are a selective unigram cache. a conditional bigram cache, and a conventional static trigram. We describe the knowledge sources used to build such a model with ARPAs official WSJ corpus. and repon on perplexity and word error rate results obtained with iL Then, three different adaptation paradigms are discussed, and an additional experiment, based on AP wire data, is used to compare them.

Subject Categories:

  • Linguistics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE