EKTB11: Neural network based text analysis models for Estonian (2018-2022)

During the last few years, the natural language processing field has experienced a considerable technological shift due to the rapid developments in artificial neural networks technology. For many text analysis tasks concerned with automatic analysis of linguistic structure, neural models have proved to be more successful than the previously used statistical models. The automatic text analysis tools developed so far for Estonian are either rule-based or statistical. Based on the literature it can be expected that by adopting neural models their performance can be improved. The goal of this project is to bring the automatic text analysis tools for Estonian up to date by transferring them to neural technologies with the goal of improving their accuracy and quality.

Principal Investigator: Kairit Sirts, PhD
Institution: Institute of Computer Science, University of Tartu
Funding: The national program “Estonian Language Technology 2018-2027”






  • Aleksei Dorkin, MA (2022)
  • ChengHan Chung, Bsc (2021-2022)
  • Hasan Tanvir, Msc (2020-2021)
  • Kairit Peekman, Bsc (2020, 2022)
  • Claudia Kittask, Msc (2019 -2021)
  • Laura-Katrin Leman, MA (2019-2020)
  • Kirill Milintsevich, Msc (2018-2020)