  Dividing sentences in chunks of words is a useful preprocessing step for
parsing, information extraction and information retrieval. (Ramshaw and Marcus,
1995) have introduced a "convenient" data representation for chunking by
converting it to a tagging task. In this paper we will examine seven different
data representations for the problem of recognizing noun phrase chunks. We will
show that the the data representation choice has a minor influence on chunking
performance. However, equipped with the most suitable data representation, our
memory-based learning chunker was able to improve the best published chunking
results for a standard data set.
