Enhanced Named Entity Recognition algorithm for financial document verification

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dc.contributor.authorToprak, Ahmet
dc.contributor.authorTuran, Metin
dc.date.accessioned2023-11-07T13:50:53Z
dc.date.available2023-11-07T13:50:53Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractMany enterprise systems are document-intensive and require extensive manual verifcation. The verifcation process has challenge in terms of time and remaining bugs. A general automatic or semi-automatic document verifcation system would be useful. However, as the nature of the natural language, the context is an important factor. In this research, the target context is selected to be the fnancial documents, which have been highly interested recently. An automatic document verifcation model based on only entities (mostly faced within fnancial documents) was experimented. The summary report was verifed with original documents, such that enti ties in the summary were searched for matching in the original documents. Verifca tion process success was evaluated by comparison of the named entity algorithms in the literature. The special Kaggle data set ready for this purpose was used for entity matching from the summary within the original documents. The average document verifcation accuracy of named entity fnding algorithms for only fnancial type documents was 85.36%, where the proposed entity recognition algorithm reached 88.80%. On the other hand, the average document verifcation time of the experi mented algorithms and the developed algorithm is 2.43 and 2.48 s respectively. As a conclusion, when both the BERT-base-cased classifcation model and rule-based approaches are applied specifc to the context, it enhances the entity verifcation process with an insignifcant time cost. Consequently, even we used limited data and rules, it is seen that there exists opportunity to automatize the document verifcation process with the support of both the BERT-base-cased classifcation model and rulebased approaches.en_US
dc.identifier.doi10.1007/s11227-023-05371-4en_US
dc.identifier.endpage19451en_US
dc.identifier.issue17en_US
dc.identifier.scopus2-s2.0-85160432023en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage19431en_US
dc.identifier.urihttps://hdl.handle.net/11467/6928
dc.identifier.urihttps://doi.org/10.1007/s11227-023-05371-4
dc.identifier.volume79en_US
dc.identifier.wosWOS:000994797400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Supercomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAutomatic document verifcation, Named Entity Recognition, Document summarization, Spell-checker, Natural language processingen_US
dc.titleEnhanced Named Entity Recognition algorithm for financial document verificationen_US
dc.typeArticleen_US

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