Meertens Institute, director; Professor of Language and Artificial Intelligence at the University of Amsterdam;

Member of the Royal Dutch Academy of Arts and Sciences; EurAI Fellow

KNAW Meertens Institute
Oudezijds Achterburgwal 185
1012 DK Amsterdam
The Netherlands

KNAW Meertens Institute
Radboud University
P.O. Box 10855
1001 EW Amsterdam
The Netherlands

Photo: Elodie Burillon / HUCOPIX

Research interests

In my research I develop machine learning and language technology. Most of my work involves the intersection of the two fields: computers that learn to understand and generate natural language. Specific interests include memory-based learning, machine translation, the relation between written and spoken language, text mining, the Dutch language, computational humanities, and its reverse, cultural AI. My CV has more detailed information.




Since January 2017 I am director of the Meertens Institute, an institute of the Royal Netherlands Academy for Arts and Sciences, studing the diversity in language and culture in the Netherlands. Together with the Huygens ING and the International Institute for Social History the three institutes form the KNAW Humanities Cluster.

I am guest professor at CLiPS, the Computational Linguistics and Psycholinguistics Research Centre at the University of Antwerp. I spent many good years at the ILK Research Group at Tilburg University, and at the Centre for Language and Speech Technology and the Centre for Language Studies at Radboud University. I am fellow of EURAI, and member of the Royal Netherlands Academy of Arts and Sciences.


Current projects

  • CLARIAH, Common Lab Research Infrastructure for the Arts and Humanities. I am a board member of this exciting new project that will continue and enlarge the digital infrastructure for the Humanities in the Netherlands.

  • Public Talks

    Past projects & highlights

    Older news items:

    Current (co-) supervised Ph.D. students

    • Martijn Bentum (with Mirjam Ernestus and Louis ten Bosch)
    • Lucas van der Deijl (with Lia van Gemert)
    • Eric Sanders
    • Roel Smeets (with Maarten De Pourcq)
    • Robbert De Troij (with Dirk Speelman, Benedikt Szmrecsanyi, and Stefan Grondelaers)
    • Chara Tsoukala (with Stefan Frank and Mirjam Boersma)
    • Jinbiao Yang (with Stefan Frank)

    Former (co-) supervised students


    Click and explore the following demos showcasing our recent work:




    Aside from papers and dissertations, our projects tend to produce software. We make a point of maximizing the availability of this software by releasing the best software projects under open source licenses. Some of our software, such as Timbl and Frog, is packaged and available in Debian Science. Other packages, particularly the ones that perform some natural language processing function, are available as webservices, usually with a web interface.

    As part of past and ongoing projects with many colleagues I was involved in developing the following software:

    Natural language processing

    • Frog: Dutch tagger-lemmatizer, morphological analyzer, and dependency parser. With the Frog development team.
    • and Dutch and English context-sensitive spelling correctors. With Maarten van Gompel, Wessel Stoop, Tanja Gaustad van Zaanen, and Monica Hajek.
    • PBMBMT: Phrase-based memory-based machine translation. With Maarten van Gompel.
    • Mbt: Memory-based tagger-generator and tagger. With Ko van der Sloot, Jakub Zavrel, and Walter Daelemans.
    • WOPR: Memory-based word prediction, language modeling, and spelling correction. Main developer: Peter Berck.

    Machine Learning

    • Timbl: Tilburg memory-based learner. With Ko van der Sloot, Walter Daelemans, and Jakub Zavrel.
    • Dimbl: Distributed Timbl, parallel k-NN classification on multi-CPU machines. Programmer: Ko van der Sloot.
    • Timpute: TiMBL-wrapper for internal database correction through imputation. Programmer: Steve Hunt.
    • paramsearch: automatic parameter optimization for various machine learning algorithms.
    • Fambl: Family-based learner, a generalized-example k-NN classifier. Reference guide.