Low-Resource Active Learning of Morphological Segmentation

• Published in: Northern European Journal of Language Technology
• Main Authors: Grönroos, Stig-Arne, Hiovain, Katri, Smit, Peter, Rauhala, Ilona, Jokinen, Kristiina, Kurimo, Mikko, Virpioja, Sami
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Linkoping University Electronic Press 2016
• Subjects: North Sámi; Sámi
• Online Access: http://dx.doi.org/10.3384/nejlt.2000-1533.1644; https://nejlt.ep.liu.se/article/download/1662/1005

Many Uralic languages have a rich morphological structure, but lack morphological analysis tools 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 with a large unannotated corpus and a small amount of annotated word forms selected using an active learning approach. We apply the procedure to two Finno-Ugric languages: Finnish and North Sámi. The semi-supervised Morfessor FlatCat method is used for statistical learning. For Finnish, we set up a simulated scenario to test various active learning query strategies. The best performance is provided by a coverage-based strategy on word initial and final substrings. For North Sámi we collect a set of humanannotated data. With 300 words annotated with our active learning setup, we see a relative improvement in morph boundary F1-score of 19% compared to unsupervised learning and 7.8% compared to random selection.