@inproceedings{dacanay-etal-2021-detail,
title = "The More Detail, the Better? {--} Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a {P}lains {C}ree / n{\^e}hiyaw{\^e}win Dictionary",
author = "Dacanay, Daniel and
Harrigan, Atticus and
Wolvengrey, Arok and
Arppe, Antti",
editor = "Mager, Manuel and
Oncevay, Arturo and
Rios, Annette and
Ruiz, Ivan Vladimir Meza and
Palmer, Alexis and
Neubig, Graham and
Kann, Katharina",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.americasnlp-1.15",
doi = "10.18653/v1/2021.americasnlp-1.15",
pages = "143--152",
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 n{\^e}hiyaw{\^e}win / 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.",
}
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%0 Conference Proceedings
%T The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary
%A Dacanay, Daniel
%A Harrigan, Atticus
%A Wolvengrey, Arok
%A Arppe, Antti
%Y Mager, Manuel
%Y Oncevay, Arturo
%Y Rios, Annette
%Y Ruiz, Ivan Vladimir Meza
%Y Palmer, Alexis
%Y Neubig, Graham
%Y Kann, Katharina
%S Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F dacanay-etal-2021-detail
%X 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.
%R 10.18653/v1/2021.americasnlp-1.15
%U https://aclanthology.org/2021.americasnlp-1.15
%U https://doi.org/10.18653/v1/2021.americasnlp-1.15
%P 143-152
Markdown (Informal)
[The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary](https://aclanthology.org/2021.americasnlp-1.15) (Dacanay et al., AmericasNLP 2021)
ACL