Low-Resource Active Learning of Morphological Segmentation

@inproceedings{Rauhala2017LowResourceAL,
  title={Low-Resource Active Learning of Morphological Segmentation},
  author={Ilona Rauhala and Sami Virpioja},
  year={2017}
}
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… CONTINUE READING
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