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# Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing

James Hammerton .hammerton@let.rug.nl
Alfa-Informatica
University of Groningen
The Netherlands
Miles Osborne sborne@cogsci.ed.ac.uk
Division of Informatics
University of Edinburgh
Scotland
Susan Armstrong usan.armstrong@issco.unige.ch
ISSCO/ETI
University of Geneva
Switzerland Walter Daelemans alter.daelemans@uia.ua.ac.be
Center for Dutch Language and Speech
University of Antwerp
Belgium

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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.

- Introduction
- Overview of Papers
  - Memory Based Shallow Parsing
  - Shallow Parsing using Specialized HMM
  - Text Chunking Based on a Generalization of Winnow
  - Shallow Parsing With PoS Taggers and Linguistic Knowledge
  - Learning Rules and their exceptions
  - Shallow Parsing using Noisy and Non-Stationary Training Material
- Conclusions
- Acknowledgements
- Bibliography
- About this document ...