Papers › SIGMORPHON 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence...
SIGMORPHON 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence Labelling for Word-Level Morpheme Segmentation
Leander Girrbach
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 “shallow segmentation” and “canonical segmentation”, 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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Morpheme Segmentaiton | UniMorph 4.0 | BiLSTM for seq labelling (Tü_Seg-1) | macro avg (subtask 1) | 96.06 | #5 of 19 | Archive leaderboard | report |
| Morpheme Segmentaiton | UniMorph 4.0 | BiLSTM for seq labelling (Tü_Seg-2) | f1 macro avg (subtask 2) | 82.07 | #16 of 19 | Archive leaderboard | report |
| Morpheme Segmentaiton | UniMorph 4.0 | BiLSTM for seq labelling (Tü_Seg-2) | lev dist (subtask 2) | 4.71 | #16 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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