Language Acquisition and Machine Learning

摘要

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)