Low-Resource Active Learning of Morphological Segmentation
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©: 2016 The Authors.
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2016
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Language
en
Pages
26
47-72
47-72
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NORTHERN EUROPEAN JOURNAL OF LANGUAGE TECHNOLOGY, Volume 4
Abstract
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.Description
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Grönroos, S-A, Hiovain, K, Smit, P, Rauhala, I, Jokinen, K, Kurimo, M & Virpioja, S 2016, ' Low-Resource Active Learning of Morphological Segmentation ', NORTHERN EUROPEAN JOURNAL OF LANGUAGE TECHNOLOGY, vol. 4, 4, pp. 47-72 . https://doi.org/10.3384/nejlt.2000-1533.1644