Genetic algorithms for feature relevance assignment in memory-based language processing

Authors:
Anne Kool;Walter Daelemans;Jakub Zavrel
Affiliations:
University of Antwerp, UIA, Antwerpen, Belgium;University of Antwerp, UIA, Antwerpen, Belgium;University of Antwerp, UIA, Antwerpen, Belgium
Venue:
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Year:
2000

Citing 8
Cited 2

Instance-Based Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
A Review and Empirical Evaluation of Feature Weighting Methods for aClass of Lazy Learning Algorithms

Artificial Intelligence Review - Special issue on lazy learning
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Forgetting Exceptions is Harmful in Language Learning

Machine Learning - Special issue on natural language learning
Further Research on Feature Selection and Classification Using Genetic Algorithms

Proceedings of the 5th International Conference on Genetic Algorithms
Weighting Features

ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development

Using genetic algorithms to discover selection criteria for contradictory solutions retrieved by CBR

ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

Research on Language and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the usefulness of evolutionary algorithms in three incarnations of the problem of feature relevance assignment in memory-based language processing (MBLP): feature weighting, feature ordering and feature selection. We use a simple genetic algorithm (GA) for this problem on two typical tasks in natural language processing: morphological synthesis and unknown word tagging. We find that GA feature selection always significantly outperforms the MBLP variant without selection and that feature ordering and weighting with GA significantly outperforms a situation where no weighting is used. However, GA selection does not significantly do better than simple iterative feature selection methods, and GA weighting and ordering reach only similar performance as current information-theoretic feature weighting methods.