Abstract
This paper describes a medicinal products and active ingredients named entity recogniser (MaNER) for Spanish technical documents. This rule-based system uses high quality and low-maintenance lexicons. Our results (F-measure 90 %) proves that dictionary-based approaches, without any deep natural language processing (e.g. POS tagging), can achieve a high performance in this task. Our system obtains better results when compared to similar systems.
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This paper has been partially supported by the Spanish Government (grant no. TIN2012-38536-C03-03 and TIN2012-31224)
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Moreno, I., Moreda, P., Romá-Ferri, M.T. (2015). MaNER: A MedicAl Named Entity Recogniser. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_40
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