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ECML
2003
Springer

Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language

9 years 2 months ago
Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language
Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the ‘right bias’ to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons.
Walter Daelemans, Véronique Hoste, Fien De
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where ECML
Authors Walter Daelemans, Véronique Hoste, Fien De Meulder, Bart Naudts
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