Abstract
We investigate the impact of input data scale in corpus-based learning using a study style of Zipfs law. In our research, Chinese word segmentation is chosen as the study case and a series of experiments are specially conducted for it, in which two types of segmentation techniques, statistical learning and rule-based methods, are examined. The empirical results show that a linear performance improvement in statistical learning requires an exponential increasing of training corpus size at least. As for the rule-based method, an approximate negative inverse relationship between the performance and the size of the input lexicon can be observed.- Anthology ID:
- L10-1134
- Volume:
- Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
- Month:
- May
- Year:
- 2010
- Address:
- Valletta, Malta
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/199_Paper.pdf
- DOI:
- Cite (ACL):
- Hai Zhao, Yan Song, and Chunyu Kit. 2010. How Large a Corpus Do We Need: Statistical Method Versus Rule-based Method. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
- Cite (Informal):
- How Large a Corpus Do We Need: Statistical Method Versus Rule-based Method (Zhao et al., LREC 2010)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/199_Paper.pdf