Propuesta de un sistema de clasificación de entidades basado en perfiles e independiente del dominio

  1. Isabel Moreno Agulló
  2. María Teresa Romá Ferri
  3. María Paloma Moreda Pozo
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2017

Issue: 59

Pages: 23-30

Type: Article

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Abstract

Named Entity Recognition and Classification (NERC) is a prerequisite to other natural language processing applications. Nevertheless, the adaptation of NERC systems is expensive given that most of them only work appropiately on the domain for which they were created. Bearing this idea in mind, a named entity classification system, which is profile and machine learning based, is evaluated to determine if the results are maintained regardeless of the domain of the training corpus. To that end, it is tested on 6 types of entities from two different domains in Spanish: general and medical. Applying techniques to balance class distribution, the difference in terms of F1 between domains is 0.02 points (F1: 50.36 versus 50.38, respectively). These results support the domain independence of our profile-based system.

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