Using CCG categories to improve Hindi dependency parsing

Bharat Ram Ambati, Tejaswini Deoskar, Mark Steedman

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


We show that informative lexical categories from a strongly lexicalised formalism such as Combinatory Categorial Grammar (CCG) can improve dependency parsing of Hindi, a free word order language. We first describe a novel way to obtain a CCG lexicon and treebank from an existing dependency treebank, using a CCG parser. We use the output of a supertagger trained on the CCGbank as a feature for a state-of-the-art Hindi dependency parser (Malt). Our results show that using CCG categories improves the accuracy of Malt on long distance dependencies, for which it is known to have weak rates of recovery.
Original languageEnglish
Title of host publicationProceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Place of PublicationSofia, Bulgaria
PublisherAssociation for Computational Linguistics
Number of pages6
Publication statusPublished - 1 Aug 2013


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