An Empirical Study of Feature Set Selection for Text Chunking

This paper presents an empirical study for improving the performance of text chunking. We focus on two issues: the problem of selecting feature spaces, and the problem of alleviating the data sparseness. To select a proper feature space, we use a heuristic and exhaustive method of evaluating the performance of models under various feature spaces. Besides, for smoothing the data sparseness, we suggest a method of combining words and word classes based on WordNet. Experimenta