An Incremental Algorithm for Transition-based CCG Parsing

Bharat Ram Ambati, Tejaswini Deoskar, Mark Johnson, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Incremental parsers have potential advantages for applications like language modeling for machine translation and speech recognition. We describe a new algorithm for incremental transition-based Combinatory Categorial Grammar parsing. As English CCGbank derivations are mostly right branching and non-incremental, we design our algorithm based on the dependencies resolved rather than the derivation. We introduce two new actions in the shift-reduce paradigm based on the idea of ‘revealing’ (Pareschi and Steedman, 1987) the required information during parsing. On the standard CCGbank test data, our algorithm achieved improvements of 0.88% in labeled and 2.0% in unlabeled F-score over a greedy non-incremental shift-reduce parser.
Original languageEnglish
Title of host publicationProceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Place of PublicationDenver, Colorado
PublisherAssociation for Computational Linguistics
Number of pages11
ISBN (Print)978-1-941643-49-5
Publication statusPublished - 1 May 2015

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