Named Entity Recognition in Turkish Bank Documents

Year 2021, Volume: 4 Issue: 2, 86 - 92, 30.11.2021

Osman Kabasakal Alev Mutlu

https://doi.org/10.34088/kojose.871873

Abstract

Named Entity Recognition (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 (CRF) model to extract named entities. The main contribution of this study is twofold: (i) we propose domain-specific features to extract entity names such as law, regulation, and reference which frequently appear in bank documents; and (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

Project Number

5190074

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