Machine Learning

Raymond J. Mooney

in The Oxford Handbook of Computational Linguistics

Published in print January 2005 | ISBN: 9780199276349
Published online September 2012 | e-ISBN: 9780191743573 | DOI:

Series: Oxford Handbooks in Linguistics

 Machine Learning

Show Summary Details


This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described by a set of features. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word-sense disambiguation and anaphora resolution. Finally, this article reviews the applications to a variety of these problems, such as morphology, part-of-speech tagging, word-sense disambiguation, syntactic parsing, semantic parsing, information extraction, and anaphora resolution.

Keywords: symbolic machine learning; supervised training examples; information extraction; syntactic parsing; semantic parsing; anaphora resolution; part-of-speech tagging

Article.  6806 words. 

Subjects: Computational Linguistics

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.