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Chinese CCGbank: Deep derivations and dependencies for Chinese CCG parsing

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If you have a question about this talk, please contact Tamara Polajnar.

We adapt the corpus conversion methodology harnessed by Hockenmaier for CCGbank, to obtain Chinese CCGbank, the first wide-coverage CCG corpus of Chinese text.

We show that the corpus conversion methodology, and thus Chinese CCGbank, is appropriate for acquiring wide-coverage models of Chinese syntax, by adapting three state-of-the-art Chinese parsers to obtain the first Chinese CCG parsing models in the literature.

We demonstrate that while the three parsers are only separated by a small margin trained on English CCGbank, a substantial gulf of 4.8% separates the same parsers trained on Chinese CCGbank. We also confirm that the gap between the states-of-the-art in English and Chinese PSG parsing can be observed in CCG parsing.

Our parsing experiments establish Chinese CCG parsing as a new and substantial challenge.

This talk is part of the NLIP Seminar Series series.

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