| Population reconstruction G Bloothooft, P Christen, K Mandemakers, M Schraagen Springer 10, 978-3, 2015 | 31 | 2015 |
| Public sentiment on governmental COVID-19 measures in Dutch social media S Wang, M Schraagen, ETK Sang, M Dastani Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020 | 29 | 2020 |
| Same Author or Just Same Topic? Towards Content-Independent Style Representations A Wegmann, M Schraagen, D Nguyen arXiv preprint arXiv:2204.04907, 2022 | 28 | 2022 |
| The CLIN27 shared task: Translating historical text to contemporary language for improving automatic linguistic annotation ETK Sang, M Bollmann, R Boschker, F Casacuberta, F Dietz, S Dipper, ... Computational Linguistics in the Netherlands Journal 7, 53-64, 2017 | 27 | 2017 |
| Transforming epilepsy research: A systematic review on natural language processing applications ANJ Yew, M Schraagen, WM Otte, E van Diessen Epilepsia 64 (2), 292-305, 2023 | 25 | 2023 |
| Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods B van Es, LC Reteig, SC Tan, M Schraagen, MM Hemker, SRS Arends, ... BMC bioinformatics 24 (1), 10, 2023 | 19 | 2023 |
| Evaluation of Named Entity Recognition in Dutch online criminal complaints MP Schraagen, MJS Brinkhuis, FJ Bex Computational Linguistics in the Netherlands Journal 7, 3-16, 2017 | 18 | 2017 |
| Aspects of record linkage M Schraagen Diss. Leiden University. http://hdl. handle. net/1887/29716, 2014 | 15* | 2014 |
| Extraction of semantic relations in noisy user-generated law enforcement data M Schraagen, F Bex 2019 IEEE 13th International Conference on Semantic Computing (ICSC), 79-86, 2019 | 14 | 2019 |
| Dutch general public reaction on governmental covid-19 measures and announcements in twitter data S Wang, M Schraagen, ETK Sang, M Dastani arXiv preprint arXiv:2006.07283, 2020 | 13 | 2020 |
| Argumentation-driven information extraction for online crime reports MP Schraagen, FJ Bex, D Odekerken, BJG Testerink International workshop on legal data analysis and mining (LeDAM 2018): CEUR …, 2018 | 11 | 2018 |
| Learning name variants from inexact high-confidence matches G Bloothooft, M Schraagen Population reconstruction, 61-83, 2015 | 11 | 2015 |
| Name fashion dynamics and social class G Bloothooft, M Schraagen Proceedings of the XXIV-International Conference of Onomastic Sciences, 2011 | 7 | 2011 |
| Predicting record linkage potential in a family reconstruction graph M Schraagen, HJ Hoogeboom Proceedings of 23rd Benelux Conference on Artificial Intelligence (BNAIC …, 2011 | 7 | 2011 |
| Record linkage using graph consistency M Schraagen, W Kosters Machine Learning and Data Mining in Pattern Recognition: 10th International …, 2014 | 6 | 2014 |
| Complete coverage for approximate string matching in record linkage using bit vectors M Schraagen 2011 IEEE 23rd International Conference on Tools with Artificial …, 2011 | 5 | 2011 |
| Evaluating Repetitions, or how to Improve your Multilingual ASR System by doing Nothing. M Schraagen, G Bloothooft LREC, 2010 | 5 | 2010 |
| Abstractive Summarization of Dutch Court Verdicts Using Sequence-to-sequence Models M Schraagen, F Bex, N Van De Luijtgaarden, D Prijs Proceedings of the Natural Legal Language Processing Workshop 2022, 76-87, 2022 | 4 | 2022 |
| Classification in a Skewed Online Trade Fraud Complaint Corpus W Kos, MP Schraagen, MJS Brinkhuis, FJ Bex Preproceedings of the 29th Benelux Conference on Artificial Intelligence …, 2017 | 4 | 2017 |
| Learning name variants from true person resolution G Bloothooft, M Schraagen Proceedings of the International Workhop on Population Reconstruction …, 2014 | 4 | 2014 |
