World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
12132
World Ranking
7415
National Ranking
51

Overview

Filip Ginter is affiliated with the University of Turku in Finland and has established a research profile primarily within the field of Computer Science. Their scholarly output is concentrated in Artificial Intelligence, with additional contributions to Molecular Biology, Information Systems, Literature and Literary Theory, and Computer Vision and Pattern Recognition.

The main research topics that Filip Ginter has focused on include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Text Readability and Simplification
  • Speech and dialogue systems
  • Multimodal Machine Learning Applications
  • Digital Humanities and Scholarship
  • Biomedical Text Mining and Ontologies

Ginter's recent publications demonstrate engagement with multilingual and computational linguistic resources as well as digital humanities perspectives. Representative papers include:

  • "Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection" (2020), published in arXiv (Cornell University)
  • "Universal Dependencies" (2025), published in Elsevier eBooks
  • "Universal Dependencies" (2025), published in HAL (Le Centre pour la Communication Scientifique Directe)
  • "The reuse of texts in Finnish newspapers and journals, 1771-1920: A digital humanities perspective" (2020), published in Historical Methods A Journal of Quantitative and Interdisciplinary History
  • "WikiBERT models: deep transfer learning for many languages" (2020), published in arXiv (Cornell University)

Frequent co-authors collaborating with Ginter are:

  • Jenna Kanerva (24 joint works)
  • Sampo Pyysalo (15 joint works)
  • Li-Hsin Chang (9 joint works)
  • Veronika Laippala (8 joint works)
  • Iiro Rastas (7 joint works)

The venues in which Ginter's work frequently appears include:

  • arXiv (Cornell University)
  • Natural Language Engineering
  • Research Square (Research Square)
  • Elsevier eBooks
  • Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

  • Universal Dependencies v1: A Multilingual Treebank Collection

    Joakim Nivre;Marie-Catherine de Marneffe;Filip Ginter;Yoav Goldberg

  • CoNLL 2018 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies

    Daniel Zeman;Jan Hajič;Martin Popel;Martin Potthast

  • Universal Stanford dependencies: A cross-linguistic typology

    Marie-Catherine de Marneffe;Timothy Dozat;Natalia Silveira;Katri Haverinen

  • Universal Dependencies 2.2

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • BioInfer: a corpus for information extraction in the biomedical domain

    Sampo Pyysalo;Filip Ginter;Juho Heimonen;Jari Björne

  • Distributional Semantics Resources for Biomedical Text Processing

    S Pyysalo;F Ginter;H Moen;T Salakoski

  • CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

    Daniel Zeman;Martin Popel;Milan Straka;Jan Hajic

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

    Antti Airola;Sampo Pyysalo;Jari Björne;Tapio Pahikkala

  • Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection

    Joakim Nivre;Marie-Catherine de Marneffe;Filip Ginter;Jan Hajic

  • Comparative analysis of five protein-protein interaction corpora

    Sampo Pyysalo;Antti Airola;Juho Heimonen;Jari Björne

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T Clark;Asma R Bankapur

  • Universal Dependencies 1.0

    Joakim Nivre;Cristina Bosco;Jinho Choi;Marie-Catherine de Marneffe

  • Extracting Complex Biological Events with Rich Graph-Based Feature Sets

    Jari Björne;Juho Heimonen;Filip Ginter;Antti Airola

  • Universal Dependencies 2.1

    Joakim Nivre;Željko Agić;Lars Ahrenberg;Lene Antonsen

  • Universal Dependencies 2.0

    Joakim Nivre;Željko Agić;Lars Ahrenberg;Maria Jesus Aranzabe

  • Universal Dependencies 2.3

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • Multilingual is not enough: BERT for Finnish

    Antti Virtanen;Jenna Kanerva;Rami Ilo;Jouni Luoma

  • Universal Dependencies 1.2

    Joakim Nivre;Željko Agić;Maria Jesus Aranzabe;Masayuki Asahara

  • Universal Dependencies 2.7

    Daniel Zeman;Joakim Nivre;Mitchell Abrams;Elia Ackermann

  • Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

Frequent Co-Authors

Tapio Salakoski
Tapio Salakoski University of Turku
Sampo Pyysalo
Sampo Pyysalo University of Turku
Joakim Nivre
Joakim Nivre Uppsala University
Jan Hajič
Jan Hajič Charles University
Christopher D. Manning
Christopher D. Manning Stanford University
Marie-Catherine de Marneffe
Marie-Catherine de Marneffe The Ohio State University
Slav Petrov
Slav Petrov Google (United States)
Barbara Plank
Barbara Plank Ludwig-Maximilians-Universität München

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