Low-Resource Active Learning of North Sámi Morphological Segmentation

  • 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

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.

Published
2015-06-17