- Shift-Reduce CCG Parsing using Neural Network Models
- The 15th Annual Meeting of the North American Chapter of Association for Computational Linguistics
- Book/source title
- The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Book/source subtitle
- NAACL HLT 2016 : proceedings of the conference : June 12-17, 2016, San Diego, California, USA
- Pages (from-to)
- Sroudsburg, PA: Association for Computational Linguistics
- Document type
- Conference contribution
- Interfacultary Research Institutes
Faculty of Science (FNWI)
- Institute for Logic, Language and Computation (ILLC)
We present a neural network based shift- reduce CCG parser, the first neural-network based parser for CCG. We also study the im- pact of neural network based tagging mod- els, 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 improve- ment 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.
- Final publisher version
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