Low-Resource Active Learning of North Sámi Morphological Segmentation

Stig-Arne Grönroos, Kristiina Jokinen, Katri Hiovain, Mikko Kurimo, Sami Virpioja

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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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Copyright (c) 2015 Stig-Arne Grönroos, Kristiina Jokinen, Katri Hiovain, Mikko Kurimo, Sami Virpioja