Introduction to special issue on machine learning approaches to shallow parsing

Authors:
James Hammerton;Miles Osborne;Susan Armstrong;Walter Daelemans
Affiliations:
Alfa-Informatica, University of Groningen, The Netherlands;Division of Informatics, University of Edinburgh, Scotland;ISSCO/ETI, University of Geneva, Switzerland;Center for Dutch Language and Speech, University of Antwerp, Belgium
Venue:
The Journal of Machine Learning Research
Year:
2002

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Abstract

This article introduces the problem of partial or shallow parsing (assigning partial syntactic structure to sentences) and explains why it is an important natural language processing (NLP) task. The complexity of the task makes Machine Learning an attractive option in comparison to the handcrafting of rules. On the other hand, because of the same task complexity, shallow parsing makes an excellent benchmark problem for evaluating machine learning algorithms. We sketch the origins of shallow parsing as a specific task for machine learning of language, and introduce the articles accepted for this special issue, a representative sample of current research in this area. Finally, future directions for machine learning of shallow parsing are suggested.