Abstract
The paper deals with the task of definition extraction from a small and noisy corpus of instructive texts. Three approaches are presented: Partial Parsing, Machine Learning and a sequential combination of both. We show that applying ML methods with the support of a trivial grammar gives results better than a relatively complicated partial grammar, and much better than pure ML approach.- Anthology ID:
- L08-1294
- Volume:
- Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
- Month:
- May
- Year:
- 2008
- Address:
- Marrakech, Morocco
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/213_paper.pdf
- DOI:
- Cite (ACL):
- Łukasz Degórski, Michał Marcińczuk, and Adam Przepiórkowski. 2008. Definition Extraction Using a Sequential Combination of Baseline Grammars and Machine Learning Classifiers. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
- Cite (Informal):
- Definition Extraction Using a Sequential Combination of Baseline Grammars and Machine Learning Classifiers (Degórski et al., LREC 2008)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/213_paper.pdf