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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.5.pdf
Export citation
@inproceedings{socolof-etal-2022-measuring,
title = "Measuring Morphological Fusion Using Partial Information Decomposition",
author = "Socolof, Michaela and
Hoover, Jacob Louis and
Futrell, Richard and
Sordoni, Alessandro and
O{'}Donnell, Timothy J.",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.5",
pages = "44--54",
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.",
}
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%0 Conference Proceedings %T Measuring Morphological Fusion Using Partial Information Decomposition %A Socolof, Michaela %A Hoover, Jacob Louis %A Futrell, Richard %A Sordoni, Alessandro %A O’Donnell, Timothy J. %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F socolof-etal-2022-measuring %X 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. %U https://aclanthology.org/2022.coling-1.5 %P 44-54
Markdown (Informal)
[Measuring Morphological Fusion Using Partial Information Decomposition](https://aclanthology.org/2022.coling-1.5) (Socolof et al., COLING 2022)
- Measuring Morphological Fusion Using Partial Information Decomposition (Socolof et al., COLING 2022)
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.