Abstract
F-measure is an indicator which has been commonly used for 25 years to evaluate classification algorithms in textmining, based on precision and recall. For classification and information retrieval, some prefer to use the break even point. Nevertheless, these measures have some inconvenient: they use a binary logic and don’t allow to apply a user (judge) assessment. This paper proposes a new approach for evaluation. First, we distinguish classification and categorization from a semantic point of view. Then, we introduce a new measure: the K-measure, which is an overall of F-measure, and allows to apply user requirements. Finally, we propose a methodology for evaluation.
This work is partially financed by MENRT for the RNTS Rhea project.
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© 2005 Springer-Verlag Berlin Heidelberg
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Nakache, D., Metais, E., Timsit, J.F. (2005). Evaluation and NLP. In: Andersen, K.V., Debenham, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2005. Lecture Notes in Computer Science, vol 3588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546924_61
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DOI: https://doi.org/10.1007/11546924_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28566-3
Online ISBN: 978-3-540-31729-6
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