Accession Number:

ADA169581

Title:

Language Acquisition and Machine Learning.

Descriptive Note:

Interim rept. Jun-Dec 85,

Corporate Author:

CALIFORNIA UNIV IRVINE DEPT OF INFORMATION AND COMPUTER SCIENCE

Personal Author(s):

Report Date:

1986-02-01

Pagination or Media Count:

40.0

Abstract:

This paper reviews recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, the authors propose four component tasks involved in learning from experience-aggregation, clustering, characterization, and storage. They then consider four common problems studied by machine learning researchers-learning from examples, heuristics learning, conceptual clustering, and learning macro-operators-describing each in terms of our framework. After this, they turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, they note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition. Author

Subject Categories:

  • Cybernetics
  • Linguistics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE