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Semi-supervised morphosyntactic classification of Old Icelandic
Kryztof Urban1, Timothy R Tangherlini1, Aurelijus Vijūnas2
1The Scandinavian Section, University of California Los Angeles, Los Angeles, California, United States of America.
We developed IceMorph, a new tool for analyzing Old Icelandic text. It uses a semi-supervised approach to accurately classify words with minimal training data.
Area of Science:
- Computational Linguistics
- Digital Humanities
- Historical Linguistics
Background:
- Analyzing historical languages like Old Icelandic presents challenges due to limited annotated data.
- Traditional methods often require extensive manual annotation, which is time-consuming and resource-intensive.
Observation:
- IceMorph leverages semi-supervised learning, combining machine-read corpora and dictionaries with a novel set of declension prototypes.
- A user-friendly web GUI facilitates expert input, allowing for data modification and augmentation.
- A machine learning module integrates prototype data, edit-distance metrics, and user feedback for continuous improvement.
Findings:
- The IceMorph system achieves competitive morphosyntactic classification accuracy.
- It demonstrates effectiveness even with a minimal amount of training data, reducing annotation burden.
Implications:
- This approach offers a scalable and efficient method for analyzing morphosyntax in under-resourced historical languages.
- IceMorph has the potential to significantly advance Old Icelandic digital scholarship and linguistic research.
- The semi-supervised methodology can be adapted for analyzing other historical or low-resource languages.
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