Enhanced Named Entity Recognition algorithm for financial document verification
Many enterprise systems are document-intensive and require extensive manual verification. The verification process has challenge in terms of time and remaining bugs. A general automatic or semi-automatic document verification system would be useful. However, as the nature of the natural language, th...
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| Vydáno v: | The Journal of supercomputing Ročník 79; číslo 17; s. 19431 - 19451 |
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01.11.2023
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | Many enterprise systems are document-intensive and require extensive manual verification. The verification process has challenge in terms of time and remaining bugs. A general automatic or semi-automatic document verification 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 financial documents, which have been highly interested recently. An automatic document verification model based on only entities (mostly faced within financial documents) was experimented. The summary report was verified with original documents, such that entities in the summary were searched for matching in the original documents. Verification 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 verification accuracy of named entity finding algorithms for only financial type documents was 85.36%, where the proposed entity recognition algorithm reached 88.80%. On the other hand, the average document verification time of the experimented algorithms and the developed algorithm is 2.43 and 2.48 s respectively. As a conclusion, when both the BERT-base-cased classification model and rule-based approaches are applied specific to the context, it enhances the entity verification process with an insignificant time cost. Consequently, even we used limited data and rules, it is seen that there exists opportunity to automatize the document verification process with the support of both the BERT-base-cased classification model and rule-based approaches. |
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| AbstractList | Many enterprise systems are document-intensive and require extensive manual verification. The verification process has challenge in terms of time and remaining bugs. A general automatic or semi-automatic document verification 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 financial documents, which have been highly interested recently. An automatic document verification model based on only entities (mostly faced within financial documents) was experimented. The summary report was verified with original documents, such that entities in the summary were searched for matching in the original documents. Verification 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 verification accuracy of named entity finding algorithms for only financial type documents was 85.36%, where the proposed entity recognition algorithm reached 88.80%. On the other hand, the average document verification time of the experimented algorithms and the developed algorithm is 2.43 and 2.48 s respectively. As a conclusion, when both the BERT-base-cased classification model and rule-based approaches are applied specific to the context, it enhances the entity verification process with an insignificant time cost. Consequently, even we used limited data and rules, it is seen that there exists opportunity to automatize the document verification process with the support of both the BERT-base-cased classification model and rule-based approaches. Many enterprise systems are document-intensive and require extensive manual verification. The verification process has challenge in terms of time and remaining bugs. A general automatic or semi-automatic document verification 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 financial documents, which have been highly interested recently. An automatic document verification model based on only entities (mostly faced within financial documents) was experimented. The summary report was verified with original documents, such that entities in the summary were searched for matching in the original documents. Verification 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 verification accuracy of named entity finding algorithms for only financial type documents was 85.36%, where the proposed entity recognition algorithm reached 88.80%. On the other hand, the average document verification time of the experimented algorithms and the developed algorithm is 2.43 and 2.48 s respectively. As a conclusion, when both the BERT-base-cased classification model and rule-based approaches are applied specific to the context, it enhances the entity verification process with an insignificant time cost. Consequently, even we used limited data and rules, it is seen that there exists opportunity to automatize the document verification process with the support of both the BERT-base-cased classification model and rule-based approaches. |
| Author | Turan, Metin Toprak, Ahmet |
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| Cites_doi | 10.1016/j.artint.2005.03.001 10.1109/ICME.2000.871093 10.1162/tacl_a_00088 10.1109/WI-IAT.2011.258 10.18653/v1/P19-1139 10.1007/978-3-642-22546-8_21 10.1109/TENCON.2015.7372818 10.1145/1571941.1571989 10.1109/ACCESS.2015.2431493 10.3115/1699510.1699512 10.1109/ICDAR.2011.66 10.1109/DAS.2018.74 10.1109/EISIC.2015.21 10.1109/ACCESS.2021.3129786 10.3115/1609822.1609823 10.1109/ICVGIP.2008.67 10.1109/DAS.2016.75 10.1109/SITIS.2015.70 10.1109/EDOC.2016.7579376 10.1109/HICSS.2004.1265265 10.1145/1321440.1321542 10.1016/j.patrec.2005.03.024 10.1075/li.30.1.03nad |
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| Keywords | Named Entity Recognition Natural language processing Spell-checker Document summarization Automatic document verification |
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| References | Naman J (2022) NER dataset. https://www.kaggle.com/namanj27/ner-dataset, Accessed 28 Dec 2022 Beusekom JV, Shafait F (2011) Distortion measurement for automatic document verification. In: 2011 International Conference on Document Analysis and Recognition, pp 289–293 Poon H, Domingos P (2009) Unsupervised semantic parsing. In: proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, pp 1–10. https://aclanthology.org/D09-1001 Petkova D, Croft WB (2007) Proximity-Based document representation for named entity retrieval. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM ’07, Association for Computing Machinery, New York, pp 731–740 SangEMeulderFIntroduction to the CoNLL-2003 shared task: language-independent named entity recognitionProc Seventh Conf Nat Lang Learn HLT-NAACL20032003142147 Garain U, Halder B (2008) On automatic authenticity verification of printed security documents. In: 2008 Sixth Indian Conference on Computer Vision, Graphics and Image Processing, pp 706–713 Mollá D, van Zaanen M, Smith D (2006) Named Entity Recognition for question answering. In: Proceedings of the Australasian Language Technology Workshop 2006, Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), Sydney, Australia, pp 51–58. https://aclanthology.org/U06-1009 Wang J-H (2011) Web-based verification on the representativeness of terms extracted from single short documents. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 3, pp 114–117 ReddySTäckströmOCollinsMTransforming dependency structures to logical forms for semantic parsingTrans Assoc Comput Linguist2016412714010.1162/tacl_a_00088 Wu C-H, Huang C-L, Hsu C-S, et al (2007) Speech retrieval using spoken keyword extraction and semantic verification. TENCON 2007–2007 IEEE Region 10 Conference, pp 1–4 Zhang Z, Han X, Liu Z, et al (2019) ERNIE: enhanced language representation with informative entities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, pp 1441–1451 BassilYA trainable summarizer with knowledge acquired from robust NLP techniquesInt J Res Rev Comput Sci (IJRRCS)20123120792557 BensefiaAPaquetTHeutteLA writer identification and verification systemPattern Recognit Lett200526132080209210.1016/j.patrec.2005.03.0241087.68677 MridhaMFLimaAANurKA survey of automatic text summarization: Progress. Process and challengesIEEE Access2021915604315607010.1109/ACCESS.2021.3129786 Roychoudhury S, Bellarykar N, Kulkarni V (2016) A NLP based framework to support document verification-as-a-service. In: 2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), pp 1–10 Takata Y, Nakamura T, Seki H (2004) Accessibility verification of WWW documents by an automatic guideline verification tool. In: 37th Annual Hawaii International Conference on System Sciences, 2004. 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In: TENCON 2015–2015 IEEE Region 10 Conference, pp 1–3 NadeauDSekineSA survey of named entity recognition and classificationLingvist Investig200730132610.1075/li.30.1.03nad Babych B, Hartley A (2003) Improving machine translation quality with automatic Named Entity Recognition. In: Proceedings of the 7th International EAMT Workshop on MT and Other Language Technology Tools, Improving MT Through Other Language Technology Tools, Resource and Tools for Building MT at EACL 2003. Budapest https://aclanthology.org/W03-2201 Hnoohom N, Chumuang N, Ketcham M (2015) Thai Handwritten verification system on documents for the investigation. 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| References_xml | – reference: Ghanmi N, Awal AM (2018) A new descriptor for pattern matching: application to identity document verification. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp 375–380 – reference: NadeauDSekineSA survey of named entity recognition and classificationLingvist Investig200730132610.1075/li.30.1.03nad – reference: Beusekom JV, Shafait F (2011) Distortion measurement for automatic document verification. In: 2011 International Conference on Document Analysis and Recognition, pp 289–293 – reference: Pariza S (2022) BBC news summary., 2022. https://www.kaggle.com/pariza/bbc-news-summary, Accessed 28 Dec – reference: HassanpourSO’ConnorMJDasAKBassiliadesNGovernatoriGPaschkeAA framework for the automatic extraction of rules from online textRule-based reasoning, programming, and applications2011Berlin, HeidelbergSpringer26628010.1007/978-3-642-22546-8_21 – reference: EtzioniOCafarellaMDowneyDUnsupervised named-entity extraction from the Web: an experimental studyArtif Intell200516519113410.1016/j.artint.2005.03.001 – reference: Elkasrawi S, Dengel A, Abdelsamad A, et al (2016) What you see is what you get? Automatic image verification for online news content. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp 114–119 – reference: Babych B, Hartley A (2003) Improving machine translation quality with automatic Named Entity Recognition. In: Proceedings of the 7th International EAMT Workshop on MT and Other Language Technology Tools, Improving MT Through Other Language Technology Tools, Resource and Tools for Building MT at EACL 2003. Budapest https://aclanthology.org/W03-2201 – reference: SangEMeulderFIntroduction to the CoNLL-2003 shared task: language-independent named entity recognitionProc Seventh Conf Nat Lang Learn HLT-NAACL20032003142147 – reference: Ando T, Yatsu H, Hisazumi K, et al (2015) Reference model of specifications toward independent verification and validation. In: TENCON 2015–2015 IEEE Region 10 Conference, pp 1–3 – reference: ReddySTäckströmOCollinsMTransforming dependency structures to logical forms for semantic parsingTrans Assoc Comput Linguist2016412714010.1162/tacl_a_00088 – reference: Zhang Z, Han X, Liu Z, et al (2019) ERNIE: enhanced language representation with informative entities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, pp 1441–1451 – reference: Poon H, Domingos P (2009) Unsupervised semantic parsing. In: proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, pp 1–10. https://aclanthology.org/D09-1001 – reference: Petkova D, Croft WB (2007) Proximity-Based document representation for named entity retrieval. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM ’07, Association for Computing Machinery, New York, pp 731–740 – reference: BassilYA trainable summarizer with knowledge acquired from robust NLP techniquesInt J Res Rev Comput Sci (IJRRCS)20123120792557 – reference: Wang J-H (2011) Web-based verification on the representativeness of terms extracted from single short documents. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 3, pp 114–117 – reference: Hnoohom N, Chumuang N, Ketcham M (2015) Thai Handwritten verification system on documents for the investigation. In: 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp 617–622 – reference: Takata Y, Nakamura T, Seki H (2004) Accessibility verification of WWW documents by an automatic guideline verification tool. In: 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the, p 10 – reference: BensefiaAPaquetTHeutteLA writer identification and verification systemPattern Recognit Lett200526132080209210.1016/j.patrec.2005.03.0241087.68677 – reference: Roychoudhury S, Bellarykar N, Kulkarni V (2016) A NLP based framework to support document verification-as-a-service. 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