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...
Published in: | Septentrio Conference Series |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Septentrio Academic Publishing
2015
|
Subjects: | |
Online Access: | https://septentrio.uit.no/index.php/SCS/article/view/3465 https://doi.org/10.7557/5.3465 |
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. Aer annotating 237 words with our active learning setup, we improve morph boundary recall over 20% with no loss of precision. |
---|