A Methodology for Feature Selection in Named Entity Recognition

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dc.contributor.authorKitoogo, Fredrick Edward
dc.contributor.authorBaryamureeba, Venansius
dc.date.accessioned2022-07-17T14:58:24Z
dc.date.available2022-07-17T14:58:24Z
dc.date.issued2007
dc.descriptionChapter 7, Page 88en_US
dc.description.abstractIn 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.en_US
dc.identifier.citationKitoogo, F. E., & Baryamureeba, V. (2007). A methodology for feature selection in named entity recognition. Strengthening the Role of ICT in Development, 88. ISBN 978-9970-02-730-9en_US
dc.identifier.isbn978-9970-02-730-9
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4208
dc.language.isoenen_US
dc.publisherFountain Publishersen_US
dc.subjectMethodologyen_US
dc.subjectFeature Selectionen_US
dc.subjectEntity Recognitionen_US
dc.titleA Methodology for Feature Selection in Named Entity Recognitionen_US
dc.typeBook chapteren_US
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