Using CCG categories to improve Hindi dependency parsing

Bharat Ram Ambati; Tejaswini Deoskar; Mark Steedman

Using CCG categories to improve Hindi dependency parsing

Bharat Ram Ambati, Tejaswini Deoskar, Mark Steedman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Place of Publication	Sofia, Bulgaria
Publisher	Association for Computational Linguistics
Pages	604-609
Number of pages	6
Publication status	Published - 1 Aug 2013

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http://www.aclweb.org/anthology/P13-2107

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title = "Using CCG categories to improve Hindi dependency parsing",

abstract = "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.",

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Using CCG categories to improve Hindi dependency parsing. / Ambati, Bharat Ram; Deoskar, Tejaswini; Steedman, Mark.

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Sofia, Bulgaria : Association for Computational Linguistics, 2013. p. 604-609.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Using CCG categories to improve Hindi dependency parsing

AU - Ambati, Bharat Ram

AU - Deoskar, Tejaswini

AU - Steedman, Mark

PY - 2013/8/1

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N2 - 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.

AB - 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.

M3 - Conference contribution

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EP - 609

BT - Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

PB - Association for Computational Linguistics

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ER -

Using CCG categories to improve Hindi dependency parsing

Abstract

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