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Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (l~mshaw 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.
Sang et al. (Fri,) studied this question.