Named Entity Recognition in Turkish Bank Documents

Authors : Osman KABASAKAL, Alev MUTLU

Pages : 86-92

Doi:10.34088/kojose.871873

View : 98 | Download : 8

Publication Date : 2021-11-30

Article Type : Research Paper

Abstract :Named Entity Recognition insert ignore into journalissuearticles values(NER); is the process of automatically recognizing entity names such as person, organization, and date in a document. In this study, we focus on bank documents written in Turkish and propose a Conditional Random Fields insert ignore into journalissuearticles values(CRF); model to extract named entities. The main contribution of this study is twofold: insert ignore into journalissuearticles values(i); we propose domain-specific features to extract entity names such as law, regulation, and reference which frequently appear in bank documents; and insert ignore into journalissuearticles values(ii); we contribute to NER research in Turkish document which is not as mature as other languages such as English and German. Experimental results based on 10-fold cross validation conducted on 551 real-life, anonymized bank documents show the proposed CRF-NER model achieves 0.962 micro average F1 score. More specifically, F1 score for the identification of law names is 0.979, regulation name is 0.850, and article no is 0.850.
Keywords : Bank Document, Conditional Random Fields, Named Entity Recognition, Natural Language Processing, Turkish Documents

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