Abstract
This paper presents an annotation tool that detects entities in the biomedical domain. By enriching the lexica of the Freeling analyzer with bio-medical terms extracted from dictionaries and ontologies as SNOMED CT, the system is able to automatically detect medical terms in texts. An evaluation has been performed against a manually tagged corpus focusing on entities referring to pharmaceutical drug-names, substances and diseases. The obtained results show that a good annotation tool would help to leverage subsequent processes as data mining or pattern recognition tasks in the biomedical domain. © Springer-Verlag 2013.
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Oronoz, M., Casillas, A., Gojenola, K., & Perez, A. (2013). Automatic annotation of medical records in spanish with disease, drug and substance names. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8259 LNCS, pp. 536–543). https://doi.org/10.1007/978-3-642-41827-3_67
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