Abstract
Words in natural language can be assigned to specific morphological categories. For example, the English word ‘apples’ can be described using morphological labels like N;PL. The conditional probabilities on such word forms given the labels would reveal for English that the morpheme ‘s’ is present almost always when the label N;PL appears. This indicates that the morphological properties of a word can be traced to its morphemes. We do not have any data resource that associates morphemes with morphological categories. We use UniMorph schema and datasets for universal morphological annotation as a source of morphological categories and morpheme segmentation. We align morphemes (or exponents) with the corresponding morphological categories based on the UniMorph schema for 12 languages. Given the multilingual nature of the task, we utilize unsupervised methods based on the \(\Delta P\) measure and IBM Models as we test out the effectiveness of alignment methods used in statistical machine translation. Our results indicate that IBM Models accurately capture the alignment asymmetries between morphemes and morphological categories under non-trivial alignment settings.
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Notes
- 1.
Here, the morphological tags should be interpreted as a bundle or list of morphological categories assigned to a word.
- 2.
- 3.
We don’t need to resolve the deficiency in IBM models 3 and 4, s.t. we don’t use the IBM 5 model; we want to model fertility, so apart from the simplest IBM1 model we use only the alignment models including fertility model, i.e. IBM 3 and 4.
- 4.
- 5.
In UniMorph segmentation data, zero morphemes are not segmented off, hence words with the inflectional ending that exhibits a zero morph are considered monomorphemic words.
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Acknowledgments
This work has been using data, tools and services provided by the LINDAT/CLARIAH-CZ Research Infrastructure (https://lindat.cz), supported by the Ministry of Education, Youth and Sports of the Czech Republic (Project No. LM2023062). This work has been supported by Charles University Research Centre program No. 24/SSH/009; project GA UK No. 101924; and partially supported by SVV project number 260 698. We would like to thank three anonymous reviewers for their very insightful feedback.
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Stephen, A., John, V., Žabokrtský, Z. (2024). Unsupervised Extraction of Morphological Categories for Morphemes. In: Nöth, E., Horák, A., Sojka, P. (eds) Text, Speech, and Dialogue. TSD 2024. Lecture Notes in Computer Science(), vol 15048. Springer, Cham. https://doi.org/10.1007/978-3-031-70563-2_19
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