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

References

• [1] Nagy I., Berend G., Vincze V., 2011. Noun compound and named entity recognition and their usability in keyphrase extraction. International Conference Recent Advances in Natural Language Processing, Hissar, Bulgaria, 12-14 September.
• [2] Rodrigo A., Perez-Iglesias J., Penas A., Garrido G., Araujo L., 2013. Answering questions about European legislation. Expert Systems with Applications, 40(15), pp. 5811-5816.
• [3] Cao T. H., Tang T. M., Chau C. K., 2012. Text clustering with named entities: a model, experimentation and realization. In Data mining: Foundations and intelligent paradigms, Springer, Berlin, Heidelberg.
• [4] Hassel M., 2003. Exploitation of named entities in automatic text summarization for Swedish. 14th Nordic Conference on Computational Linguistics, Reykjavik, Iceland, 30-31 May.
• [5] Grishman R., Sundheim B. M., 1996. Message Understanding Conference – 6: A brief history. The 16th International Conference on Computational Linguistics, Copenhagen, Denmark, 5-9 August.
• [6] Black W. J., Rinaldi F., Mowatt D., 1998. FACILE: Description of the NE System Used for MUC-7. 7th Message Understanding Conference, Fairfax, Virginia, 29 April – 1 May.
• [7] Aone C., Halverson L., Hampton T., Ramos-Santacruz M., 1998. SRA: Description of the IE2 system used for MUC-7. 7th Message Understanding Conference, Fairfax, Virginia, 29 April – 1 May.
• [8] Nadeau D., Turney P. D., Matwin S., 2006. Unsupervised named-entity recognition: Generating gazetteers and resolving ambiguity. 19th Canadian Conference on Artificial Intelligence, Quebec, Canada, 7-9 June.
• [9] Özkaya S., Diri B., 2011. Named entity recognition by conditional random fields from Turkish informal texts. 19th Signal Processing and Communications Applications Conference, Antalya, 20-22 April.
• [10] Lin W., Ji D., Lu Y., 2017. Disorder recognition in clinical texts using multi-label structured SVM. BMC bioinformatics, 18(1), 75, pp. 1-11.
• 11] Zhang M., Geng G., Chen J., 2020. Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations. Entropy, 22(2), pp. 252.
• [12] Zhu Q., Li X., Conesa A., Pereira C., 2018. GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text. Bioinformatics, 34(9), pp. 1547-1554.
• [13] Korvigo I., Holmatov M., Zaikovskii A., Skoblov M., 2018. Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules. Journal of cheminformatics, 10(1), pp. 1-10.
• [14] Li J., Sun A., Han J., Li C., 2020. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering.
• [15] Chen Y., Zhou C., Li T., Wu H., Zhao X., Ye K., Liao J., 2019. Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training. Journal of Biomedical Informatics, 96, pp. 103252.
• [16] Leitner E., Rehm G., Moreno-Schneider J., 2019. Fine-grained Named Entity Recognition in Legal Documents. 15th International Conference on Semantic Systems, Karlsruhe, Germany, 9-12 September.
• [17] Leaman R., Wei C. H., Lu Z., 2015. tmChem: a high performance approach for chemical named entity recognition and normalization. Journal of cheminformatics, 7(S1), S3.
• [18] Cucerzan S., Yarowsky D., 1999. Language independent named entity recognition combining morphological and contextual evidence. In 1999 joint SIGDAT conference on empirical methods in natural language processing and very large corpora.
• [19] Küçük D., 2009. Named entity recognition experiments on Turkish texts. In International Conference on Flexible Query Answering Systems. Springer, Berlin, Heidelberg.
• [20] Cekınel R. F., Ağriman M., Karagöz P., Yilmaz B., 2019. Named Entity Recognition with Conditional Random Fields on Turkish News Dataset: Revisiting the Features. 27th Signal Processing and Communications Applications Conference, Sivas, Turkey, 24-26 April.
• [21] Farmakiotou D., Karkaletsis V., Koutsias J., Sigletos G., Spyropoulos C. D., Stamatopoulos P., 2000. Rule-based named entity recognition for Greek financial texts. In Proceedings of the Workshop on Computational lexicography and Multimedia Dictionaries (COMLEX 2000).
• [22] Sheikh M., Conlon S., 2012. A rule-based system to extract financial information. Journal of Computer Information Systems, 52(4), pp. 10-19.
• [23] Wang S., Xu R., Liu B., Gui L., Zhou Y., 2014. Financial named entity recognition based on conditional random fields and information entropy. In 2014 International Conference on Machine Learning and Cybernetics, Lanzhou, China, 13-16 July
• [24] Alvarado J. C. S., Verspoor K., Baldwin T., 2015. Domain adaption of named entity recognition to support credit risk assessment. In Proceedings of the Australasian Language Technology Association Workshop 2015
• [25] Bayraktar O., Temizel T. T., 2008. Person name extraction from Turkish financial news text using local grammar-based approach. In 2008 23rd International Symposium on Computer and Information Sciences (pp. 1-4). IEEE.
• [26] Leitner E., Rehm G., Moreno-Schneider J., 2019. Fine-grained Named Entity Recognition in Legal Documents. In International Conference on Semantic Systems (pp. 272-287). Springer, Cham.
• [27] Vardhan H., Surana N., Tripathy B. K., 2020. Named-Entity Recognition for Legal Documents. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 469-479), Jaipur, India. Springer, Singapore, 13-15 February.