A comparative study of classifier combination methods applied to NLP tasks

Authors:
Fernando Enríquez;José A. Troyano;Fermín L. Cruz;F. Javier Ortega
Affiliations:
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, Sevilla, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, Sevilla, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, Sevilla, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, Sevilla, Spain
Venue:
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Year:
2011

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Abstract

There are many classification tools that can be used for various NLP tasks, although none of them can be considered the best of all since each one has a particular list of virtues and defects. The combination methods can serve both to maximize the strengths of the base classifiers and to reduce errors caused by their defects improving the results in terms of accuracy. Here is a comparative study on the most relevant methods that shows that combination seems to be a robust and reliable way of improving our results.