Many problems in NLP require solving a cascade of subtasks. Traditional pipeline approaches yield to error propagation and prohibit joint training/decoding between subtasks. Existing solutions to this problem do not guarantee non-violation of hard-constraints imposed by subtasks and thus give rise to inconsistent results, especially in cases where segmentation task precedes labeling task. We present a method that performs joint decoding of separately trained Conditional Random Field (CRF) models, while guarding against violations of hard-constraints. Evaluated on Chinese word segmentation and part-of-speech (POS) tagging tasks, our proposed method achieved state-of-the-art performance on both the Penn Chinese Treebank and First SIGHAN Bakeoff datasets. On both segmentation and POS tagging tasks, the proposed method consistently improves over baseline methods that do not perform joint decoding. 1

This paper investigates new design options for the feature space of a dependency parser. We focus on one of the simplest and most efficient architectures, based on a deterministic shift-reduce algorithm, trained with the perceptron. By adopting second-order feature maps, the primal form of the perceptron produces models with comparable accuracy to more complex architectures, with no need for approximations. Further gains in accuracy are obtained by designing features for parsing extracted from semantic annotations generated by a tagger. We provide experimental evaluations on the Penn Treebank. 1

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

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