Low-Resource Active Learning of North Sámi Morphological Segmentation

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...

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Bibliographic Details
Published in:Septentrio Conference Series
Main Authors: Grönroos, Stig-Arne, Jokinen, Kristiina, Hiovain, Katri, Kurimo, Mikko, Virpioja, Sami
Format: Article in Journal/Newspaper
Language:English
Published: Septentrio Academic Publishing 2015
Subjects:
North Sámi
Sámi
Online Access:https://septentrio.uit.no/index.php/SCS/article/view/3465
https://doi.org/10.7557/5.3465
Description
Summary: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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