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Shift-Reduce CCG Parsing using Neural Network Models

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http://www.aclweb.org/anthology/N16-1052
Original languageEnglish
Title of host publicationProceedings of NAACL-HLT 2016
PublisherAssociation for Computational Linguistics
Pages447-453
Number of pages7
ISBN (Print)978-1-941643-91-4
Publication statusPublished - 2016
Event15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - San Diego, United States
Duration: 12 Jun 201617 Jun 2016
http://naacl.org/naacl-hlt-2016/
http://naacl.org/naacl-hlt-2016/

Conference

Conference15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2016
CountryUnited States
CitySan Diego
Period12/06/1617/06/16
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Abstract

We present a neural network based shift reduce CCG parser, the first neural-network based parser for CCG. We also study the impact of neural network based tagging models, and greedy versus beam-search parsing, by using a structured neural network model. Our greedy parser obtains a labeled F-score
of 83.27%, the best reported result for greedy CCG parsing in the literature (an improvement of 2.5% over a perceptron based greedy parser) and is more than three times faster. With a beam, our structured neural network model gives a labeled F-score of 85.57% which is 0.6% better than the perceptron based counterpart.

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