Named Entity Classification Based on Profiles: A Domain Independent Approach

  • Isabel MorenoEmail author
  • M. T. Romá-Ferri
  • Paloma Moreda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10260)


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.


Named Entity Classification Profile Domain independent 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Isabel Moreno
    • 1
    Email author
  • M. T. Romá-Ferri
    • 2
  • Paloma Moreda
    • 1
  1. 1.Department of Software and Computing SystemsUniversity of AlicanteAlicanteSpain
  2. 2.Department of NursingUniversity of AlicanteAlicanteSpain

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