Machine Learning Approaches

Abstract

The usefulness and feasibility of automatically training a syntactic wordclass tagger instead of hand-crafting it motivated a large body of work on statistical and rule-learning approaches to the problem. Syntactic wordclass taggers trained on corpora are claimed to be equally accurate as, and more robust and more portable than, hand-crafted systems¹. Moreover, development time is considerably faster. Recently, inductive machine learning approaches such as connectionist learning algorithms, decision tree induction and case-based learning have also been applied to the syntactic wordclass disambiguation problem. In some cases these approaches have interesting properties not present in existing statistical and rule-based approaches.