Morpho Challenge Evaluation Using a Linguistic Gold Standard

  • Mikko Kurimo
  • Mathias Creutz
  • Matti Varjokallio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5152)


In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are basic vocabulary units suitable for different tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling. Because in unsupervised morpheme analysis the morphemes can have arbitrary names, the analyses are here evaluated by a comparison to a linguistic gold standard by matching the morpheme-sharing word pairs. The data sets were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic analysis.


Morphological analysis Machine learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mikko Kurimo
    • 1
  • Mathias Creutz
    • 1
  • Matti Varjokallio
    • 1
  1. 1.Adaptive Informatics Research CentreHelsinki University of TechnologyFinland

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