Publications

Selected publication list of Jan C. Scholtes — books, journal articles, and conference papers spanning text mining, NLP, information retrieval, LegalTech, and agentic AI systems.

Books and Book Chapters

  • Scholtes, J.C. (2021). The LegalTech Bridge (in Dutch). In Meesterlijk. Liber Amicorum Prof. dr. H.J. van den Herik. H.M. Custers, F. Dechesne & S. van der Hof (Eds.).
  • Scholtes, J.C., & van den Herik, H.J. (2021). Big Data Analytics for eDiscovery. In R. Vögl (Ed.), Big Data Law. Edgar Publishing. ISBN 978-1-78897-281-9
  • Scholtes, J.C., & van den Herik, H.J. (2019). Big Data Analytics for Legal Fact Finding. In L.R. van den Berg et al. (Eds.), Recht en Technology, vraagstukken van de digitale revolutie. Boom Juridisch.
  • Scholtes, J.C. (2009). Text Mining, the next step in search technology; Finding what doesn’t seem to be there, or finding who doesn’t want to be found. Inaugural lecture, Maastricht University. Download (1535 downloads)
  • Scholtes, J.C. (1995). Artificial Neural Networks in Information Retrieval in a Libraries Context. EC DG-XIII.
  • Scholtes, J.C. (1993). Neural Networks in Natural Language Processing and Information Retrieval. PhD Thesis, University of Amsterdam. Download

Journal Articles and Preprints

  • Shingjergji, K., Celebi, R., Scholtes, J., & Dumontier, M. (2021). Relation extraction from DailyMed structured product labels by optimally combining crowd, experts and machines. Journal of Biomedical Informatics, 122, 103902. doi.org/10.1016/j.jbi.2021.103902
  • Scholtes, J.C., van der Zee, Y., & Westerhoud, M. (2021). Science helps auditors take on the data challenge. European Court of Auditors Journal, 2021(1), 170–177.
  • van der Zee, Y., Scholtes, J.C., Westerhoud, M., & Rossi, J. (2021). Code Word Detection in Fraud Investigations using a Deep-Learning Approach. arXiv preprint. arXiv:2103.09606
  • Scholtes, J.C. (2020). Text-Mining and eDiscovery for Big-Data Audits. European Court of Auditors Journal, 2020(1), 133–140.
  • Loesch, J., van Lier, I., de Boer, A., Scholtes, J., Dumontier, M., & Celebi, R. (2024). Automated identification of healthier food substitutions through a combination of graph neural networks and nutri-scores. Journal of Food Composition and Analysis, 125, Article 105829. doi.org/10.1016/j.jfca.2023.105829
  • Sheth, S., de Kok, J., Imkamp, M., Scholtes, J., van der Horst, I., van Bussel, B., & van Rosmalen, F. (2024). Cleaning nursing notes improves the correlation between notes and SOFA score. De Intensivist, 32(4). de-intensivist.nl

Conference Papers

2025

  • Kats, T., van der Putten, P., & Scholtes, J. (2025). Relevance Feedback Strategies for Reducing Review Effort in Recall-Oriented Neural Information Retrieval. In F.A. Oliehoek, M. Kok, & S. Verwer (Eds.), Artificial Intelligence and Machine Learning — BNAIC/Benelearn 2023, Revised Selected Papers. Springer, Vol. 2187 CCIS, pp. 22–39. doi.org/10.1007/978-3-031-74650-5_2
  • Maka, P., Semerci, Y.C., Scholtes, J., & Spanakis, G. (2025). You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models. EMNLP 2025, pp. 27414–27437. doi.org/10.18653/v1/2025.emnlp-main.1394
  • Maka, P., Semerci, Y.C., Scholtes, J., & Spanakis, G. (2025). Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models. COLING 2025 — 31st International Conference on Computational Linguistics, pp. 6348–6377. doi.org/10.48550/arXiv.2412.11187
  • Issam, A., Semerci, Y.C., Scholtes, J., & Spanakis, G. (2025). A Representation Level Analysis of NMT Model Robustness to Grammatical Errors. Findings of ACL 2025, Vienna, pp. 8579–8601. doi.org/10.18653/v1/2025.findings-acl.451
  • Issam, A., Semerci, Y.C., Scholtes, J., & Spanakis, G. (2025). DTW-Align: Bridging the Modality Gap in End-to-End Speech Translation with Dynamic Time Warping Alignment. Proceedings of the 10th Conference on Machine Translation (WMT 2025), Suzhou, pp. 191–199. doi.org/10.18653/v1/2025.wmt-1.11

2024

  • Hornig, B., Pera, M.S., & Scholtes, J. (2024). Misinformation in video recommendations: an exploration of Top-N recommendation algorithms. CEUR Workshop Proceedings, 3677, 54–67. ceur-ws.org
  • Maka, P., Semerci, Y.C., Scholtes, J., & Spanakis, G. (2024). Sequence Shortening for Context-Aware Machine Translation.
  • Issam, A., Semerci, Y.C., Scholtes, J., & Spanakis, G. (2024). Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters. IWSLT 2024 — 21st International Conference on Spoken Language Translation, pp. 346–358. aclanthology.org/2024.iwslt-1.36

2021

  • Carranza-Tejada, G., Scholtes, J.C., & Spanakis, G. (2021). A study of BERT’s processing of negations to determine sentiment. BNAIC/Benelearn, Luxembourg, November 2021.

2019

  • Enendu, S., Scholtes, J., Smeets, J., Hiemstra, D., & Theune, M. (2019). Predicting Semantic Labels of Text Regions in Heterogeneous Document Images. DIR 2019, Amsterdam.
  • Hiemstra, D., Scholtes, J., Smeets, J., Enendu, S., & Theune, M. (2019). Predicting Semantic Labels of Text Regions in Heterogeneous Document Images. Poster, DIR 2019, November 29, Amsterdam.
  • Enendu, S., Scholtes, J., Smeets, J., Hiemstra, D., & Theune, M. (2019). Predicting Semantic Labels of Text Regions in Heterogeneous Document Images. KONVENS 2019, Erlangen-Nürnberg.
  • Gerolemou, Z., & Scholtes, J. (2019). Target-Based Sentiment Analysis as a Sequence-Tagging Task. BNAIC, Brussels, November 2019.
  • Scholtes, J.C., & van den Herik, H.J. (2019). Big Data Analytics for Legal Fact-Finding. Mordenate Lustrum Congress, Leiden University, May 2019.

2018

  • Helling, T.J., Takes, F.W., & Scholtes, J.C. (2018). A community-aware approach for identifying node anomalies in complex networks. 7th International Conference on Complex Networks and Their Applications, Cambridge, December 2018.
  • Heinrichs, B., & Scholtes, J.C. (2018). Detecting Anomalous Events over Time Using RDF Triple Extraction and a Dynamic Implementation of OddBall. BNAIC, Den Bosch, November 2018.

2016

  • Borggrewe, K., & Scholtes, J.C. (2016). Domain-Independent Method for Entity Resolution by determining Textual Similarities with a Support Vector Machine. BNAIC, Amsterdam, November 2016.
  • Smeets, J., Scholtes, J.C., Rasterhoff, C., & Schravemaker, M. (2016). SMTP: Stedelijk Museum Text Mining Project. Digital Humanities Conference, Kraków, July 2016.
  • Smeets, J., Scholtes, J.C., Rasterhoff, C., & Schravemaker, M. (2016). SMTP: Stedelijk Museum Text Mining Project. DHBenelux, Luxembourg, June 2016.

2015

  • Tannenbaum, M., Fischer, A., & Scholtes, J.C. (2015). Dynamic Topic Detection and Tracking using Non-Negative Matrix Factorization. BNAIC, Hasselt, November 2015.

2013

  • Scholtes, J.C., van Cann, T.H.W., & Mack, M. (2013). The Impact of Incorrect Training Sets and Rolling Collections on Technology-Assisted Review. ICAIL 2013, DESI V Workshop, Rome, June 2013.
  • Scholtes, J.C., & van Cann, T.H.W. (2013). Improving Machine Learning Input for Automatic Document Classification with Natural Language Processing. BNAIC, Delft, November 2013.

2012

  • Maghsoodi, A., Sevenster, M., Scholtes, J., & Nabaltov, G. (2012). Automatic Sentence-based Classification of Free-text Breast Cancer Radiology Reports. 25th IEEE CBMS 2012.
  • Maes, F., & Scholtes, J.C. (2012). Authorship Disambiguation and Alias Resolution in Email Data. BNAIC, Maastricht, October 2012.

2009–2008

  • Scholtes, J.C. (2009). Text Mining: The next step in Search Technology. ICAIL 2009/DESI III Workshop, Barcelona, June 2009.
  • Scholtes, J.C. (2008). Is there a role for Sentiment Mining in Robot-Human Communications? 1st International Conference on Human-Robots Personal Relationships, June 2008.

Working Papers and Preprints

  • Sibirtsev, A., & Scholtes, J.C. (2015). Advanced Methods for Fraud Detection. Working paper.
  • Smeets, J., Scholtes, J.C., Rasterfoff, C., & Schravemaker, M. (2015). SMTP: Stedelijk Museum Text Mining Project. Working paper.
  • Trinnes, O., & Scholtes, J.C. (2014). Visualization and Interactive Navigation for Search Result Lists in Law Enforcement and Investigations Applications. Working paper.
  • Kruif, J., Rooijackers, M.L.M., & Scholtes, J. (2013). Van Fruin naar Frijhoff: Een netwerkanalyse van 135 jaar vaktijdschrift. Working paper.
  • Küppers, B., & Scholtes, J. (2012). Application of Artificial Intelligence search techniques to cryptanalysis. Working paper.
  • Maghsoodia, A., Scholtes, J., & Seventer, M. (2012). The Effect of Anaphora and Co-reference Resolution on Sentence-based Classification of Breast Cancer Radiology Reports. Working paper.

1987–1992

  • Scholtes, J.C. (1992). Resolving Linguistic Ambiguities with a Neural Data-Oriented Parsing (DOP) System. Artificial Neural Networks 2, Vol. 2, pp. 1347–1350. Elsevier.
  • Scholtes, J.C. (1992). Neural Nets versus Statistics in Information Retrieval. SPIE Conference on Applications of Artificial Neural Networks III, Orlando, FL.
  • Scholtes, J.C., & Bloembergen, S. (1992). The Design of a Neural Data-Oriented Parsing (DOP) Model. IJCNN, Baltimore.
  • Scholtes, J.C., & Bloembergen, S. (1992). Corpus Based Parsing with a Self-Organizing Neural Net. IJCNN, Beijing.
  • Scholtes, J.C. (1991). Unsupervised Context Learning in Natural Language Processing. IJCNN, Seattle, Vol. 1, pp. 107–112.
  • Scholtes, J.C. (1991). Learning Simple Semantics by Self-Organization. AAAI Spring Symposium on Connectionist Natural Language Processing, Palo Alto, pp. 78–83.
  • Scholtes, J.C. (1991). Recurrent Kohonen Self-Organization in Natural Language Processing. Artificial Neural Networks (T. Kohonen et al., Eds.), pp. 1751–1754. Elsevier.
  • Scholtes, J.C. (1990). Trends in Neurolinguistics. IEEE Symposium on Neural Networks, Delft, pp. 69–86.
  • van den Herik, H.J., Scholtes, J.C., & Verhoest, C.R.J. (1988). The Design of a Knowledge-Based Optical-Character Recognition System. SCS, Nice, pp. 350–358.
  • Henseler, J., van den Herik, H.J., Kerckhoffs, E.J.H., Koppelaar, H., Scholtes, J.C., & Verhoest, C.R.J. (1988). Knowledge-Based Parallelism in Optical Character Recognition. Summer Computer Simulation Conference, Seattle, pp. 14–20.