A methodology for feature selection in named entity recognition

dc.creatorKitoogo, Fredrick Edward
dc.creatorBaryamureeba, Venansius
dc.date2012-09-26T13:43:57Z
dc.date2012-09-26T13:43:57Z
dc.date2007
dc.date.accessioned2018-09-04T12:32:54Z
dc.date.available2018-09-04T12:32:54Z
dc.descriptionConference paper which can be downloaded in fulltext from the conference organiser's website
dc.descriptionIn 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.
dc.identifierKitoogo, F. E. and Baryamureeba, V. (2007, Augus 5-8). A methodology for feature selection in named entity recognition. 3rd Annual International Conference on Computing and ICT Research: Computer Science, pp.88-100
dc.identifier978-9970-02-730-9
dc.identifierhttp://hdl.handle.net/10570/702
dc.identifier.urihttp://hdl.handle.net/10570/702
dc.languageen
dc.publisherFountain Publishers Kampala
dc.relationSREC;07
dc.subjectnamed entity recognition
dc.subjectmultiobjective genetic algorithm
dc.subjectmachine learning algorithm
dc.titleA methodology for feature selection in named entity recognition
dc.typeConference paper
Files