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date: 02 July 2020

Abstract and Keywords

This chapter introduces 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. It also briefly reviews unsupervised learning, in which new concepts are formed from unannotated examples by clustering them into coherent groups. 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. Applications to a variety of these problems are reviewed.

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

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