Morphological Segmentation Inside-Out
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Abstract (arXiv)
Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output. In many cases, however, proper morphological analysis requires hierarchical structure -- especially in the case of derivational morphology. In this work, we introduce a discriminative, joint model of morphological segmentation along with the orthographic changes that occur during word formation. To the best of our knowledge, this is the first attempt to approach discriminative segmentation with a context-free model. Additionally, we release an annotated treebank of 7454 English words with constituency parses, encouraging future research in this area.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2023Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Modelcited by 9
- 2021Computational Morphology with Neural Network Approachescited by 2
- 2022Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentationcited by 2
- 2024Recent advancements in computational morphology : A comprehensive surveycited by 2
- 2024Recent advancements in computational morphology : A comprehensive survey8 Jun 2024
- 2023Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model23 Oct 2023
- 2022Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation5 Jan 2022
- 2021Computational Morphology with Neural Network Approaches19 May 2021