Shift-Reduce CCG Parsing using Neural Network Models
| Authors |
|
|---|---|
| Publication date | 2016 |
| Host editors |
|
| Book title | NAACL HLT 2016 : The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
| Book subtitle | Proceedings of the Conference : June 12-17, 2016, San Diego, California, USA |
| ISBN |
|
| Event | The 15th Annual Meeting of the North American Chapter of Association for Computational Linguistics |
| Pages (from-to) | 447-453 |
| Publisher | Stroudsburg, PA: The Association for Computational Linguistics |
| Organisations |
|
| Abstract |
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.
|
| Document type | Conference contribution |
| Language | English |
| Published at | https://doi.org/10.18653/v1/N16-1052 |
| Downloads |
N16-1052
(Final published version)
|
| Permalink to this page | |