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.

Martha Palmer, whose guidance and support, and the personal time she has invested throughout my time as a graduate student, are much appreciated. Dan Gildea has been instrumental in helping me develop and focus my dissertation research topic. I would also like to thank Mitch Marcus, Fernando Pereira, and Ben Taskar, for accepting my invitation to participate in my thesis dissertation as members of the thesis committee. Finally, I would like to thank my wife, my parents, and my two brothers for their unwavering love, affection and support. ii