Abstract
In this study, we present a hybrid named entity recognizer for Turkish, which is based on a previously proposed rule based recognizer. Since rule based systems for speci¯c domains require their knowledge sources to be manually revised when ported to other domains, we turn the rule based recognizer into a hybrid one so that it learns from annotated data and improves its knowledge sources accordingly. Both the hybrid recognizer and its predecessor are evaluated on the same corpora and the hybrid recognizer achieves comparably better results. The current study is signi¯cant since it presents the¯rst hybrid {manually engineered and learning{ named entity recognizer for Turkish texts
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Küçük, D., Yazici, A. (2011). A Hybrid Named Entity Recognizer for Turkish with Applications to Different Text Genres. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_23
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DOI: https://doi.org/10.1007/978-90-481-9794-1_23
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