Abstract
In this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional Random Fields to perform both named entity identification and classification, and Maximum Entropy to classify given entities. Machine-learning approaches outperformed knowledge-based techniques on categories where sufficient annotated data was available. We showed that the use of external features (unsupervised clusters, information from ontology and taxonomy) improved the results significantly.- Anthology ID:
- L14-1226
- 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:
- 2518–2523
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/236_Paper.pdf
- DOI:
- Cite (ACL):
- Cyril Grouin. 2014. Biomedical entity extraction using machine-learning based approaches. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2518–2523, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Biomedical entity extraction using machine-learning based approaches (Grouin, LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/236_Paper.pdf