Literature and Weblinks

Literature

Christian Boulanger, and Anastasiia Iurshina (2022): “Extracting Bibliographic References from Footnotes with EXcite-Docker”, in: Tobias Backes, Anastasiia Iurshina, and Philipp Mayr (eds.): Proceedings of the Workshop on Understanding Literature References in Academic Full Text. CEUR Workshop Proceedings 3220, pp. 26–33. http://ceur-ws.org/Vol-3220/#paper3
Alessia Cioffi, and Silvio Peroni (2022): “Structured references from PDF articles: assessing the tools for bibliographic reference extraction and parsing”. DOI:10.48550/arXiv.2205.14677
Giovanni Colavizza, and Matteo Romanello (2019): “Citation Mining of Humanities Journals. The Progress to Date and the Challenges Ahead”, in: Journal of European Periodical Studies 4/1, pp. 36-53. DOI:10.21825/jeps.v4i1.10120
Maud Ehrmann, Matteo Romanello, Alex Flückinger, and Simon Clematide (2020): “Extended Overview of CLEF HIPE 2020. Named Entity Processing on Historical Newspapers”, in: Conference and Labs of the Evaluation Forum (eds.): Proceedings of the Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum. DOI:10.5281/zenodo.4117565
Maud Ehrmann, Matteo Romanello, Antoine Doucet, and Simon Clematide (2022a): “HIPE 2022. Participation Guidelines. Identifying Historical People, Places and Other Entities. Shared Task on Named Entity Recognition and Linking in Multilingual Historical Documents.” v1.0. DOI: 10.5281/zenodo.6045662
Maud Ehrmann, Matteo Romanello, Sven Najem-Meyer, Antoine Doucet, and Simon Clematide (2022b): “Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents”, in: Guglielmo Faggioli, Nicola Ferro, Allan Hanbury, and Martin Potthast (eds.): Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings 3180. DOI:10.5281/zenodo.6979577
Sehrish Iqbal, Saeed-Ul Hassan, Naif Radi Aljohani, Salem Alelyani, Raheel Nawaz, and Lutz Bornmann (2021): “A Decade of In-Text Citation Analysis Based on Natural Language Processing and Machine Learning Techniques: An Overview of Empirical Studies”, in: Scientometrics 126/8, pp. 6551–6599. DOI:10.1007/s11192-021-04055-1
Danny Rodrigues Alves, Giovanni Colavizza, and Frédéric Kaplan (2018): “Deep Reference Mining From Scholarly Literature in the Arts and Humanities”, in: Frontiers in Research Metrics and Analytics 3/21. DOI:10.3389/frma.2018.00021

Other web resources

Cristian Arteaga (2022), “Understand BLOOM, the Largest Open-Access AI, and Run It on Your Local Computer”, Blog entry on Towards Data Science Blog from 2022-08-06, https://towardsdatascience.com/run-bloom-the-largest-open-access-ai-model-on-your-desktop-computer-f48e1e2a9a32 (accessed 2023-01-10)
BigLAM (2022): “BigLAM: BigScience Libraries, Archives and Museums. 🤗 Hugging Face x 🌸 BigScience initiative to create open source community resources for LAMs.” https://huggingface.co/biglam
Duke Libraries Center for Data and Visualization Sciences (2020): “Automated Tagging of Historical, Non-English Sources with Named Entity Recognition (NER): A Resource”, 31. August 2020. http://blogs.library.duke.edu/data/2020/08/31/automated-tagging-of-historical-non-english-sources-with-named-entity-recognition-ner-a-resource/.
Embeddia project (2022). Embeddia is a European Union H2020 project ended in March 2022 that created NLP tools focusing on European under-represented languages and that had for objective to improve the accessibility of these tools to the general public and to media enterprises. http://embeddia.eu/
Sebastian Ruder (2021), “Recent Advances in Language Model Fine-tuning”, http://ruder.io/recent-advances-lm-fine-tuning.