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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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
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spelling ftunitroemsoe:oai:ojs.henry.ub.uit.no:article/3465 2023-05-15T17:40:07+02:00 Low-Resource Active Learning of North Sámi Morphological Segmentation Grönroos, Stig-Arne Jokinen, Kristiina Hiovain, Katri Kurimo, Mikko Virpioja, Sami 2015-06-17 application/pdf https://septentrio.uit.no/index.php/SCS/article/view/3465 https://doi.org/10.7557/5.3465 eng eng Septentrio Academic Publishing https://septentrio.uit.no/index.php/SCS/article/view/3465/3389 https://septentrio.uit.no/index.php/SCS/article/view/3465 doi:10.7557/5.3465 Copyright (c) 2015 Stig-Arne Grönroos, Kristiina Jokinen, Katri Hiovain, Mikko Kurimo, Sami Virpioja Septentrio Conference Series; No 2 (2015): First International Workshop on Computational Linguistics for Uralic Languages; 20–33 2387-3086 10.7557/scs.2015.2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2015 ftunitroemsoe https://doi.org/10.7557/5.3465 https://doi.org/10.7557/scs.2015.2 2021-08-16T16:49:15Z 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. Article in Journal/Newspaper North Sámi Sámi University of Tromsø: Septentrio Academic Publishing Septentrio Conference Series 2 20
institution Open Polar
collection University of Tromsø: Septentrio Academic Publishing
op_collection_id ftunitroemsoe
language English
description 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.
format Article in Journal/Newspaper
author Grönroos, Stig-Arne
Jokinen, Kristiina
Hiovain, Katri
Kurimo, Mikko
Virpioja, Sami
spellingShingle Grönroos, Stig-Arne
Jokinen, Kristiina
Hiovain, Katri
Kurimo, Mikko
Virpioja, Sami
Low-Resource Active Learning of North Sámi Morphological Segmentation
author_facet Grönroos, Stig-Arne
Jokinen, Kristiina
Hiovain, Katri
Kurimo, Mikko
Virpioja, Sami
author_sort Grönroos, Stig-Arne
title Low-Resource Active Learning of North Sámi Morphological Segmentation
title_short Low-Resource Active Learning of North Sámi Morphological Segmentation
title_full Low-Resource Active Learning of North Sámi Morphological Segmentation
title_fullStr Low-Resource Active Learning of North Sámi Morphological Segmentation
title_full_unstemmed Low-Resource Active Learning of North Sámi Morphological Segmentation
title_sort low-resource active learning of north sámi morphological segmentation
publisher Septentrio Academic Publishing
publishDate 2015
url https://septentrio.uit.no/index.php/SCS/article/view/3465
https://doi.org/10.7557/5.3465
genre North Sámi
Sámi
genre_facet North Sámi
Sámi
op_source Septentrio Conference Series; No 2 (2015): First International Workshop on Computational Linguistics for Uralic Languages; 20–33
2387-3086
10.7557/scs.2015.2
op_relation https://septentrio.uit.no/index.php/SCS/article/view/3465/3389
https://septentrio.uit.no/index.php/SCS/article/view/3465
doi:10.7557/5.3465
op_rights Copyright (c) 2015 Stig-Arne Grönroos, Kristiina Jokinen, Katri Hiovain, Mikko Kurimo, Sami Virpioja
op_doi https://doi.org/10.7557/5.3465
https://doi.org/10.7557/scs.2015.2
container_title Septentrio Conference Series
container_issue 2
container_start_page 20
_version_ 1766140925017325568

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