@inproceedings{girrbach-2022-sigmorphon,
    title = "{SIGMORPHON} 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence Labelling for Word-Level Morpheme Segmentation",
    author = "Girrbach, Leander",
    editor = "Nicolai, Garrett  and
      Chodroff, Eleanor",
    booktitle = "Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.sigmorphon-1.13/",
    doi = "10.18653/v1/2022.sigmorphon-1.13",
    pages = "124--130",
    abstract = "We propose a sequence labelling approach to word-level morpheme segmentation. Segmentation labels are edit operations derived from a modified minimum edit distance alignment. We show that sequence labelling performs well for {\textquotedblleft}shallow segmentation{\textquotedblright} and {\textquotedblleft}canonical segmentation{\textquotedblright}, achieving 96.06 f1 score (macroaveraged over all languages in the shared task) and ranking 3rd among all participating teams. Therefore, we conclude that sequence labelling is a promising approach to morpheme segmentation."
}