Accession Number:

ADA459920

Title:

Two Algorithms for Learning the Parameters of Stochastic Context-Free Grammars

Descriptive Note:

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE

Personal Author(s):

• Heeringa, Brent
• Oates, Tim

Report Date:

2001-01-01

Pagination or Media Count:

7.0

Abstract:

Stochastic context-free grammars SCFGs are often used to represent the syntax of natural languages. Most algorithms for learning them require storage and repeated processing of a sentence corpus. The memory and computational demands of such algorithms are illsuited for embedded agents such as a mobile robot. Two algorithms are presented that incrementally learn the parameters of stochastic context-free grammars as sentences are observed. Both algorithms require a fixed amount of space regardless of the number of sentence observations. Despite using less information than the inside-outside algorithm, the algorithms perform almost as well.

Descriptors:

• *ALGORITHMS
• *STOCHASTIC PROCESSES
• *CONTEXT FREE GRAMMARS
• DISTRIBUTION
• ROBOTS
• MOBILE
• EMBEDDING
• WORDS(LANGUAGE)
• CHARTS
• STORAGE
• LEARNING
• SYNTAX
• NATURAL LANGUAGE
• PARSERS

Subject Categories:

• Linguistics
• Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE