A methodology for feature selection in named entity recognition
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Fountain Publishers Kampala
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Conference paper which can be downloaded in fulltext from the conference organiser's website
In this paper a methodology for feature selection in named entity recognition is proposed. Unlike traditional named entity recognition approaches which mainly consider accuracy improvement as the sole objective, the innovation here is manifested in the use of a multiobjective genetic algorithm which is employed for feature selection basing on various aspects including error rate reduction and time taken for evaluation, and also demonstrating the use of Pareto optimization. The proposed method is evaluated in the context of named entity recognition, using three different data sets and a K-nearest Neighbour machine learning algorithm. Comprehensive experiments demonstrate the feasibility of the methodology.
In this paper a methodology for feature selection in named entity recognition is proposed. Unlike traditional named entity recognition approaches which mainly consider accuracy improvement as the sole objective, the innovation here is manifested in the use of a multiobjective genetic algorithm which is employed for feature selection basing on various aspects including error rate reduction and time taken for evaluation, and also demonstrating the use of Pareto optimization. The proposed method is evaluated in the context of named entity recognition, using three different data sets and a K-nearest Neighbour machine learning algorithm. Comprehensive experiments demonstrate the feasibility of the methodology.
Keywords
named entity recognition, multiobjective genetic algorithm, machine learning algorithm