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Beyond MaltParser -- Recent Advances in Transition-Based Dependency Parsing

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If you have a question about this talk, please contact Ekaterina Kochmar.

The transition-based approach to dependency parsing has become popular thanks to its simplicity and efficiency. Systems like MaltParser achieve linear-time parsing with projective dependency trees using locally trained classifiers to predict the next parsing action and greedy best-first search to retrieve the optimal parse tree, assuming that the input sentence has been morphologically disambiguated using a part-of-speech tagger. In this talk, I survey recent developments in transition-based dependency parsing that address some of the limitations of the basic transition-based approach. First, I discuss different methods for extending the coverage to non-projective trees, which are required for linguistic adequacy in many languages. Secondly, I show how globally trained classifiers and beam search can be used to mitigate error propagation and enable richer feature representations. Finally, I present a model for joint tagging and parsing that leads to improvements in both tagging and parsing accuracy as compared to the standard pipeline approach.

This talk is part of the NLIP Seminar Series series.

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