Utilizing annotated wikipedia article titles to improve a rule-based named entity recognizer for Turkish

Abstract

Named entity recognition is one of the information extraction tasks which aims to identify named entities such as person/ location/organization names along with some numeric and temporal expressions in free natural language texts. In this study, we target at named entity recognition from Turkish texts on which information extraction research is considerably rare compared to other well-studied languages. The effects of utilizing annotated Wikipedia article titles to enrich the lexical resources of a rule-based named entity recognizer for Turkish are discussed after evaluating the enriched named entity recognizer against its initial version. The evaluation results demonstrate that the presented extension improves the recognition performance on different text genres, particularly on historical and financial news text sets for which the initial recognizer has not been engineered for. The current study is significant as it is the first study to address the utilization of Wikipedia articles as an information source to improve named entity recognition on Turkish texts. © 2013 Springer-Verlag Berlin Heidelberg.