The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary

Daniel Dacanay, Atticus Harrigan, Arok Wolvengrey, Antti Arppe


Abstract
One problem in the task of automatic semantic classification is the problem of determining the level on which to group lexical items. This is often accomplished using pre-made, hierarchical semantic ontologies. The following investigation explores the computational assignment of semantic classifications on the contents of a dictionary of nêhiyawêwin / Plains Cree (ISO: crk, Algonquian, Western Canada and United States), using a semantic vector space model, and following two semantic ontologies, WordNet and SIL’s Rapid Words, and compares how these computational results compare to manual classifications with the same two ontologies.
Anthology ID:
2021.americasnlp-1.15
Volume:
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
Month:
June
Year:
2021
Address:
Online
Venues:
AmericasNLP | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
143–152
Language:
URL:
https://aclanthology.org/2021.americasnlp-1.15
DOI:
10.18653/v1/2021.americasnlp-1.15
Bibkey:
Cite (ACL):
Daniel Dacanay, Atticus Harrigan, Arok Wolvengrey, and Antti Arppe. 2021. The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary. In Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, pages 143–152, Online. Association for Computational Linguistics.
Cite (Informal):
The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary (Dacanay et al., AmericasNLP 2021)
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PDF:
https://aclanthology.org/2021.americasnlp-1.15.pdf