Abstract
This paper presents a Named Entity Classification system, which uses profiles and machine learning based on [6]. Aiming at confirming its domain independence, it is tested on two domains: general - CONLL2002 corpus, and medical - DrugSemantics gold standard. Given our overall results (CONLL2002, F1 = 67.06; DrugSemantics, F1 = 71.49), our methodology has proven to be domain independent.
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\(\frac{W}{2}\) words after and before the entity.
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Acknowledgments
This paper has been supported by the Spanish Government (TIN2015-65100-R; TIN2015-65136-C02-2-R), Generalitat Valenciana (PROMETEOII/2014/001) and BBVA Foundation (FUNDACIONBBVA2-16PREMIOI).
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Moreno, I., Romá-Ferri, M.T., Moreda, P. (2017). Named Entity Classification Based on Profiles: A Domain Independent Approach. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_15
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DOI: https://doi.org/10.1007/978-3-319-59569-6_15
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