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.
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}
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