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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Moreno, I., Romá-Ferri, M. T., & Moreda, P. (2017). Named entity classification based on profiles: A domain independent approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10260 LNCS, pp. 142–146). Springer Verlag. https://doi.org/10.1007/978-3-319-59569-6_15
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