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Extraction of Numerical Facts from German Texts to Enrich Internal Audit Data

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  • First Online:
Artificial Intelligence Tools and Applications in Embedded and Mobile Systems (ICTA-EMOS 2022)

Part of the book series: Progress in IS ((PROIS))

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Abstract

Large-scale automated data processing is usually only possible for internal auditors in the case of structured data. Unstructured data, such as facts contained in texts, on the other hand, are often processed manually and using sampling. This, in turn, can increase the risk of disregarding relevant information during an audit. To address this risk, we present an approach that can be used to extract numerical facts along with their associated entities and relations from German texts and convert them into a format that can be processed by audit tools. The algorithm developed for this purpose follows a rule-based logic and was evaluated using 4637 sentences from 50 German annual reports. The results show that in more than 75% of all cases, the entity and relation of a numeric value within the sentence could be determined correctly.

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Notes

  1. 1.

    At the time of the conference in November 2022.

  2. 2.

    https://spacy.io/usage/linguistic-features#sbd.

  3. 3.

    https://spacy.io/models/de#de_dep_news_trf.

  4. 4.

    https://huggingface.co/bert-base-german-cased.

  5. 5.

    https://spacy.io/usage/linguistic-features#:$\sim$:text=useful%20tool%20for-,information%20 extraction,-%2C%20especially%20when%20combined.

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Author information

Authors and Affiliations

  1. University of Oldenburg, Oldenburg, Germany

    Gerrit Schumann & Jorge Marx Gómez

Authors
  1. Gerrit Schumann
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  2. Jorge Marx Gómez
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Correspondence to Gerrit Schumann .

Editor information

Editors and Affiliations

  1. University of Oldenburg, Oldenburg, Germany

    Jorge Marx Gómez

  2. The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

    Anael Elikana Sam

  3. The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

    Devotha Godfrey Nyambo

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Schumann, G., Marx Gómez, J. (2024). Extraction of Numerical Facts from German Texts to Enrich Internal Audit Data. In: Marx Gómez, J., Elikana Sam, A., Godfrey Nyambo, D. (eds) Artificial Intelligence Tools and Applications in Embedded and Mobile Systems. ICTA-EMOS 2022. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-031-56576-2_16

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