Low-Resource Active Learning of Morphological Segmentation
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 ma...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Linköping University Electronic Press
2016-03-01
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| Series: | Northern European Journal of Language Technology |
| Online Access: | https://nejlt.ep.liu.se/article/view/1662 |
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| Summary: | 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.
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| ISSN: | 2000-1533 |