Work

A General Technique to Train Language Models on Language Models

Year: 2005

Type: article

Abstract: We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal. This is a substantial generalization of an existing algorithm to train an n-gram model on the basis of a probabilistic context-free grammar.

Source: Computational Linguistics

DOI: 10.1162/0891201054223986

Author: Mark-Jan Nederhof

Institution: University of Groningen

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek

OpenAlex ID: W2002343100

FWCI: 2.396

Cites: 19

Cited by: 18

Related to: 10