We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.

In recent years, research in parsing has concentrated on weakening the locality of parsing decisions: history-based parsing (Black et al. 92) uses previous parse decisions on which they condition the current decision. We propose a new approach...