An Incremental Algorithm for Transition-based CCG Parsing

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

An Incremental Algorithm for Transition-based CCG Parsing

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

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

Abstract

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 language	English
Title of host publication	Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Place of Publication	Denver, Colorado
Publisher	Association for Computational Linguistics
Pages	53-63
Number of pages	11
ISBN (Print)	978-1-941643-49-5
Publication status	Published - 1 May 2015

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N15-1006Final published version, 512 KB

http://www.aclweb.org/anthology/N15-1006

GRAMPLUS: Grammar-Based robust natural language processingGRAMPLUS: Grammar-Based robust natural language processing
Steedman, M. (Principal Investigator)
EU government bodies
1/06/10 → 31/05/15
Project: Research
Xperience - 'Robotes Bootstrapped through Learning from Experience'
Steedman, M. (Principal Investigator), Geib, C. (Co-investigator) & Petrick, R. (Co-investigator)
EU government bodies
1/01/10 → 31/12/15
Project: Research

Cite this

@inproceedings{4920bda0651f4b54a7e86a90ae003fcd,

title = "An Incremental Algorithm for Transition-based CCG Parsing",

abstract = "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 {\textquoteleft}revealing{\textquoteright} (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.",

author = "Ambati, \{Bharat Ram\} and Tejaswini Deoskar and Mark Johnson and Mark Steedman",

year = "2015",

month = may,

day = "1",

language = "English",

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Ambati, BR, Deoskar, T, Johnson, M & Steedman, M 2015, An Incremental Algorithm for Transition-based CCG Parsing. in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Denver, Colorado, pp. 53-63. <http://www.aclweb.org/anthology/N15-1006>

An Incremental Algorithm for Transition-based CCG Parsing. / Ambati, Bharat Ram; Deoskar, Tejaswini; Johnson, Mark et al.
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Denver, Colorado: Association for Computational Linguistics, 2015. p. 53-63.

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

TY - GEN

T1 - An Incremental Algorithm for Transition-based CCG Parsing

AU - Ambati, Bharat Ram

AU - Deoskar, Tejaswini

AU - Johnson, Mark

AU - Steedman, Mark

PY - 2015/5/1

Y1 - 2015/5/1

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AB - 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.

M3 - Conference contribution

SN - 978-1-941643-49-5

SP - 53

EP - 63

BT - Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

PB - Association for Computational Linguistics

CY - Denver, Colorado

ER -

An Incremental Algorithm for Transition-based CCG Parsing

Abstract

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Projects

GRAMPLUS: Grammar-Based robust natural language processingGRAMPLUS: Grammar-Based robust natural language processing

Xperience - 'Robotes Bootstrapped through Learning from Experience'

Cite this