Summary of the paper
| Title | Biomedical Entity Extraction using Machine-Learning Based Approaches |
|---|---|
| Authors | Cyril Grouin |
| 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. |
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Full paper  |
Biomedical Entity Extraction using Machine-Learning Based Approaches |
| Bibtex | @InProceedings{GROUIN14.236, author = {Cyril Grouin}, title = {Biomedical Entity Extraction using Machine-Learning Based Approaches}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |