Abstract
In this paper, we describe how a Constraint Grammar with linguist-written rules can be optimized and ported to another language using a Machine Learning technique. The effects of rule movements, sorting, grammar-sectioning and systematic rule modifications are discussed and quantitatively evaluated. Statistical information is used to provide a baseline and to enhance the core of manual rules. The best-performing parameter combinations achieved part-of-speech F-scores of over 92 for a grammar ported from English to Danish, a considerable advance over both the statistical baseline (85.7), and the raw ported grammar (86.1). When the same technique was applied to an existing native Danish CG, error reduction was 10% (F=96.94).- Anthology ID:
- L14-1228
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 4483–4487
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/24_Paper.pdf
- DOI:
- Cite (ACL):
- Eckhard Bick. 2014. ML-Optimization of Ported Constraint Grammars. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4483–4487, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- ML-Optimization of Ported Constraint Grammars (Bick, LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/24_Paper.pdf