\title{Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing}
\author{James Hammerton \email j.hammerton@let.rug.nl \\
\addr 
Alfa-Informatica \\
University of Groningen \\
The Netherlands \\
\AND
Miles Osborne \email osborne@cogsci.ed.ac.uk \\
\addr 
Division of Informatics \\
University of Edinburgh \\
Scotland \\
\AND
Susan Armstrong \email susan.armstrong@issco.unige.ch \\
\addr 
ISSCO/ETI \\
University of Geneva \\
Switzerland
\AND
Walter Daelemans \email walter.daelemans@uia.ua.ac.be \\
\addr 
Center for Dutch Language and Speech \\
University of Antwerp \\
Belgium
}


\begin{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.