In many natural language processing (NLP) tasks a large amount of unlabelled data is available while labelled data is hard to attain. Bootstrapping techniques have been shown to be very successful on a variety of NLP tasks using only a small amount of supervision. In this research we have studied different bootstrapping techniques that separate the training step of the algorithm from the decoding step which produces the argmax label on test data. We then explore generative models trained in the conventional way using the EM algorithm but we use an initialization step and a decoding techniques similar to the Yarowsky bootstrapping algorithm. The new approach is tested on the named entity classification and word sense disambiguation tasks and has shown significant improvement over previous generative models.
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Thesis advisor: Sarkar, Anoop
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