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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.
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.
Original language | English |
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Title of host publication | Proceedings of NAACL-HLT 2016 |
Publisher | Association for Computational Linguistics |
Pages | 447-453 |
Number of pages | 7 |
ISBN (Print) | 978-1-941643-91-4 |
Publication status | Published - 2016 |
Event | 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - San Diego, United States Duration: 12 Jun 2016 → 17 Jun 2016 http://naacl.org/naacl-hlt-2016/ http://naacl.org/naacl-hlt-2016/ |
Conference
Conference | 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
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Abbreviated title | NAACL HLT 2016 |
Country/Territory | United States |
City | San Diego |
Period | 12/06/16 → 17/06/16 |
Internet address |
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Dive into the research topics of 'Shift-Reduce CCG Parsing using Neural Network Models'. Together they form a unique fingerprint.Projects
- 2 Finished
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Xperience - 'Robotes Bootstrapped through Learning from Experience'
Steedman, M., Geib, C. & Petrick, R.
1/01/10 → 31/12/15
Project: Research