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Incremental CCG parsing and its applications

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In this talk, we first present an incremental algorithm for transition-based CCG parsing. As previously available shift-reduce CCG parsers use CCGbank derivations which are mostly right branching and non-incremental, we design our algorithm based on the dependencies resolved rather than the derivation. Our novel algorithm builds a dependency graph in parallel to the CCG derivation which is used for revealing the unbuilt structure without backtracking.

We then show the usefulness of an incremental CCG parser for predicting relative sentence complexity. Given a pair of sentences from wikipedia and simple wikipedia, we build a classifier which predicts if one sentence is simpler/complex than the other. We show that features from a CCG parser in general and incremental CCG parser in particular are more useful than a chart-based phrase structure parser both in terms of speed and accuracy.

This talk is part of the NLIP Seminar Series series.

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