Measuring Morphological Fusion Using Partial Information Decomposition

Michaela Socolof, Jacob Louis Hoover, Richard Futrell, Alessandro Sordoni, Timothy J. O’Donnell


Abstract
Morphological systems across languages vary when it comes to the relation between form and meaning. In some languages, a single meaning feature corresponds to a single morpheme, whereas in other languages, multiple meaning features are bundled together into one morpheme. The two types of languages have been called agglutinative and fusional, respectively, but this distinction does not capture the graded nature of the phenomenon. We provide a mathematically precise way of characterizing morphological systems using partial information decomposition, a framework for decomposing mutual information into three components: unique, redundant, and synergistic information. We show that highly fusional languages are characterized by high levels of synergy.
Anthology ID:
2022.coling-1.5
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
44–54
Language:
URL:
https://aclanthology.org/2022.coling-1.5
DOI:
Bibkey:
Cite (ACL):
Michaela Socolof, Jacob Louis Hoover, Richard Futrell, Alessandro Sordoni, and Timothy J. O’Donnell. 2022. Measuring Morphological Fusion Using Partial Information Decomposition. In Proceedings of the 29th International Conference on Computational Linguistics, pages 44–54, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Measuring Morphological Fusion Using Partial Information Decomposition (Socolof et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.5.pdf