A Test Report on a Lightweight Pipeline for Named Entity Recognition
Using Machine Learning Models for Metadata Enrichment on Abstracts in iDAI.bibliography
https://doi.org/10.34780/s6tar918
Abstract
This article aims to encourage infrastructure-focused work units and teams with limited prior exposure to machine learning to actively explore current technologies and gain practical insight into their possibilities and limitations. The approach presented here can aid in the development of viable concepts and give a clearer understanding of technological feasibility, with a focus on practical solutions. To this end, the article presents a test scenario, applying Named Entity Recognition (NER) to abstracts in iDAI.bibliography – the catalogue of the DAI libraries. The approach uses a lightweight pipeline built on freely available models, a simple code base, standard hardware, and copyright-compliant methods, demonstrating how automated processing can meaningfully reduce human effort and improve the quality of the entries.
Keywords
Named Entity Recognition, Metadata Enrichment, 'Hugging Face Transformers' pipeline
References
P. Baumeister, Das neue maschinenlesbare Zeitschriftenmodell des Deutschen Archäologischen Instituts. Ein Werkstattbericht, FdAI 2022/1, § 1–67, https://doi.org/10.34780/cf6e-prcf
E. Balnaves – L. Bultrini – A. Cox – R. Uzwyshyn (ed.), New Horizons in Artificial Intelligence in Libraries (Berlin 2025), https://doi.org/10.1515/9783111336435
S. Ben Tahar – Ph. von Rummel – K. Mansel – H. Möller – T. Mukai – M. Aoudi – M. Dinies – Th. Lappi – J. Peters – S. Trixl – S. Büchner, Henchir Bourgou (Djerba, Tunesien). Stratigraphie und Fundvorlage einer Sondage im Zentrum der antiken Siedlung (8. Jh. v. Chr. – 2. Jh. n. Chr.), AA 2021/2, § 1–178, https://doi.org/10.34780/f05j-59fd
A. Brandsen, Digging in Documents: Using Text Mining to Access the Hidden Knowledge in Dutch Archaeological Excavation Reports (2022, February 15), https://hdl.handle.net/1887/3274287
A. Brandsen – S. Verberne – K. Lambers – M. Wansleeben, Can BERT Dig It? Named Entity Recognition for Information Retrieval in the Archaeology Domain, Journal on Computing and Cultural Heritage 15, 3, Article 51 (September 2022), https://doi.org/10.1145/3497842
J. Büchel – J. F. Engler – A. Mertens, Gesuchte Datenkompetenzen in Deutschland, in: Vierteljahresschrift zur empirischen Wirtschaftsforschung, Jahrgang 50, 2023, Nr. 2, 3–17, https://doi.org/10.2373/1864-810X.23-02-01
M. Dreyer, Results of the ZKI Top Trends Survey Conducted by the ZKI Strategy and Organisation Working Group for the Year 2025, https://doi.org/10.5281/zenodo.14904518
M. Dreyer, ZKI Top Trends Survey 2026, https://doi.org/10.5281/zenodo.18520259
R. Jurowetzki – D. S. Hain – K. Wirtz et al., The Private Sector is Hoarding AI Researchers: What Implications for Science?, AI & Soc 40, 4145–4152 (2025), https://doi.org/10.1007/s00146-024-02171-z
A. Kasprzik, Aufbau eines produktiven Dienstes für die automatisierte Inhaltserschließung an der ZBW – ein Status- und Erfahrungsbericht, o-bib – Das offene Bibliotheksjournal 10, 1, (2023), 1–13, https://doi.org/10.5282/o-bib/5903
A. Kasprzik, Transferring Applied Machine Learning Research into Subject Indexing Practice, in: Balnaves et al. 2025, 199–212, https://doi.org/10.1515/9783111336435-015
A. Kose – B. Engels – M. Trümper, Die Basilike Stoa an der Agora von Thera. Rekonstruktion ihrer Entwicklung auf Basis stratigraphischer Grabungen, AA 2022/1, § 1–109, https://doi.org/10.34780/tiq0-a22r
N. Maslej – L. Fattorini – R. Perrault – V. Parli – A. Reuel – E. Brynjolfsson – J. Etchemendy – K. Ligett – T. Lyons – J. Manyika – J. C. Niebles – Y. Shoham – R. Wald – J. Clark, The AI Index 2024 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024, https://doi.org/10.48550/arXiv.2405.19522
N. Maslej – L. Fattorini – R. Perrault – Y. Gil – V. Parli – N. Kariuki – E. Capstick – A. Reuel – E. Brynjolfsson – J. Etchemendy – K. Ligett – T. Lyons – J. Manyika – J. C. Niebles – Y. Shoham – R. Wald – T. Walsh – A. Hamrah – L. Santarlasci – J. Betts Lotufo – A. Rome – A. Shi – S. Oak, The AI Index 2025 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025, https://doi.org/10.48550/arXiv.2504.07139
S. Menzel – H. Schnaitter – J. Zinck – V. Petras – Cl. Neudecker – K. Labusch – El. Leitner – G. Rehm, Named Entity Linking mit Wikidata und GND – Das Potenzial handkuratierter und strukturierter Datenquellen für die semantische Anreicherung von Volltexten, in: M. Franke-Maier – A. Kasprzik – A. Ledl – H. Schürmann, Qualität in der Inhaltserschließung (Berlin 2021) 229–258, https://doi.org/10.1515/9783110691597-012
C. Osborne – J. Ding – H. R. Kirk, The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub, Journal of Computational Social Science 7, 2024, 2067–2105, https://doi.org/10.1007/s42001-024-00300-8
U. R. Pol – P. S. Vadar – T. T. Moharekar, Hugging Face: Revolutionizing AI and NLP, International Journal for Research in Applied Science and Engineering Technology 12(8),2024, 1121-1124, https://doi.org/10.22214/ijraset.2024.64023
C. Saccucci – A. Potter, Assessing Machine Learning for Cataloging at the Library of Congress, in: Balnaves et al. 2025, 227–238, https://doi.org/10.1515/9783111336435-017
M. A. Suryani – S. Karmakar – B. Mathiak, Exploration of Hugging Face Models by Heterogeneous Information Network and Linking Across Scholarly Repositories, in: L. M. Aiello – T. Chakraborty – S. Gaito (eds), Social Networks Analysis and Mining. ASONAM 2024. Lecture Notes in Computer Science, vol 15213 (2025), https://doi.org/10.1007/978-3-031-78548-1_27
C. Tzochev, The Architecture of the 4th Century B.C. Monumental Tomb at Starosel, AA 2021/2, § 1–120, https://doi.org/10.34780/d5bi-h53t
B. S. Weers, Automatisierte Inhaltserschließung an der Bibliothek des Max-Planck-Instituts für Mathematik in den Naturwissenschaften (Leipzig 2025), http://doi.org/10.33968/9783966270786-00
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