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FICC 2023: Advances in Information and Communication pp 33–40Cite as

Visual Named Entity Validation for Short Names in Financial Domain with Fine-Tuned BERT and Data Augmentation

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 651))

Abstract

Validating OCR-extracted financial entities against a financial organization’s codified data is a very challenging semantic textual similarity task because of limited context, short text, presence of abbreviations/acronyms, and OCR errors. To study this problem, we built a synthetic dataset with images that contain pseudo financial entities for OCR; model-generated short names with abbreviations/acronyms for validation. With fine-tuned BERT and data augmentation, we achieved top notch performance on this task and compared with several baseline systems.

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Notes

  1. 1.

    https://github.com/Belval/TextRecognitionDataGenerator.

  2. 2.

    https://aws.amazon.com/textract/.

  3. 3.

    List of colloquial names for universities and colleges in the United States from Wikipedia, https://rb.gy/bqoklh.

References

  1. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2018). arXiv preprint arXiv:1810.04805

  2. Dolan, W.B., Brockett, C.: Automatically constructing a corpus of sentential paraphrases. In: Proceedings of the Third International Workshop on Paraphrasing (IWP2005) (2005)

    Google Scholar 

  3. Hamdi, A., Jean-Caurant, A., Sidère, N., Coustaty, M., Doucet, A.: Assessing and minimizing the impact of OCR quality on named entity recognition. In: Hall, M., Merčun, T., Risse, T., Duchateau, F. (eds.) TPDL 2020. LNCS, vol. 12246, pp. 87–101. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-54956-5_7

    Chapter  Google Scholar 

  4. Han, M., Zhang, X., Yuan, X., Jiang, J., Yun, W., Gao, C.: A survey on the techniques, applications, and performance of short text semantic similarity. Concurrency Comput. Pract. Exp. 33(5), e5971 (2021)

    Article  Google Scholar 

  5. Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al.: A practical guide to support vector classification (2003)

    Google Scholar 

  6. Lawrie, D., Mayfield, J., Etter, D.: Building OCR/NER test collections. In: Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France, pp. 4639–4646. European Language Resources Association, May 2020

    Google Scholar 

  7. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)

    Article  Google Scholar 

  8. Nakov, P., et al.: Semeval-2017 task 3: community question answering. arXiv preprint arXiv:1912.00730 (2019)

  9. Wibisono Prakoso, D., Abdi, A., Amrit, C.: Short text similarity measurement methods: a review. Soft Computing, pp. 1–25 (2021)

    Google Scholar 

  10. Ramos, J., et al.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 29–48. Citeseer (2003)

    Google Scholar 

  11. Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. In: Proceedings of EMNLP-IJCNLP (2019)

    Google Scholar 

  12. Rodriquez, K.J., Bryant, M., Blanke, T., Luszczynska, M.: Comparison of named entity recognition tools for raw ocr text. In: Konvens, pp. 410–414 (2012)

    Google Scholar 

  13. Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. arXiv preprint arXiv:1702.03814 (2017)

  14. Wilcoxon, F.: Individual comparisons by ranking methods. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics, pp. 196–202. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_16

  15. Wolf, T., et al.: Huggingface’s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)

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Acknowledgments

We thank Ryan A. Griffin for pulling the internal data, Victor Lo and Alec Bethune for their valuable support. We thank all reviewers for their insightful comments!

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Authors and Affiliations

  1. Fidelity Investments, Boston, MA, 02210, USA

    Chen Lin, Yourong Xu & Hui Su

Authors
  1. Chen Lin
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  2. Yourong Xu
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  3. Hui Su
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Corresponding author

Correspondence to Chen Lin .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Lin, C., Xu, Y., Su, H. (2023). Visual Named Entity Validation for Short Names in Financial Domain with Fine-Tuned BERT and Data Augmentation. In: Arai, K. (eds) Advances in Information and Communication. FICC 2023. Lecture Notes in Networks and Systems, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-031-28076-4_4

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