Low-Resource Active Learning of North Sámi Morphological Segmentation

Authors

  • Stig-Arne Grönroos Department of Signal Processing and Acoustics Aalto University
  • Kristiina Jokinen Institute of Behavioural Sciences University of Helsinki
  • Katri Hiovain Institute of Behavioural Sciences University of Helsinki
  • Mikko Kurimo Department of Signal Processing and Acoustics Aalto University
  • Sami Virpioja Department of Information and Computer Science Aalto University

DOI:

https://doi.org/10.7557/5.3465

Abstract

Many Uralic languages have a rich morphological structure, but lack tools of morphological analysis needed for efficient language processing. While creating a high-quality morphological analyzer requires a significant amount of expert labor, data-driven approaches may provide sufficient quality for many applications.We study how to create a statistical model for morphological segmentation of North Sámi language with a large unannotated corpus and a small amount of human-annotated word forms selected using an active learning approach. For statistical learning, we use the semi-supervised Morfessor Baseline and FlatCat methods. Aer annotating 237 words with our active learning setup, we improve morph boundary recall over 20% with no loss of precision.

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Published

2015-06-17

How to Cite

Grönroos, S.-A., Jokinen, K., Hiovain, K., Kurimo, M., & Virpioja, S. (2015). Low-Resource Active Learning of North Sámi Morphological Segmentation. Septentrio Conference Series, (2), 20–33. https://doi.org/10.7557/5.3465

Issue

No. 2 (2015): First International Workshop on Computational Linguistics for Uralic Languages

Section

Articles

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