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Experiments on phrasal chunking in NLP using exponentiated gradient for structured prediction

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Exponentiated Gradient (EG) updates were originally introduced in (Kivinen and Warmuth, 1997) in the context of online learning algorithms. EG updates were shown by (Collins et al., 2008) to provide fast batch and online algorithms for learning a max-margin classifier. They show that EG can converge quickly due to multiplicative updates, and that EG updates can be factored into tractable components for structured prediction tasks where the number of output labels is exponential in the size of the input. In this project, we implement EG for a Natural Language Processing structured prediction task of phrasal chunking (finding noun phrases, and other phrases in text) and we compare the performance of EG with other discriminative learning algorithms that have state of the art results on this task.
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