The performance of machine learning algorithms can be improved by combining the output of different systems. In this paper we apply this idea to the recognition of noun phrases.We generate different classifiers by using different representations of the data. By combining the results with voting techniques described in (Van Halteren et.al. 1998) we manage to improve the best reported performances on standard data sets for base noun phrases and arbitrary noun phrases.
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Erik F. Tjong Kim Sang (2000) studied this question.
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