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Products for grant FN-271111-20

FN-271111-20
Documentation and speech corpus development for Gitksan [git]
Clarissa Forbes, Arizona Board of Regents

Grant details: https://apps.neh.gov/publicquery/main.aspx?f=1&gn=FN-271111-20

Gitksan FST (Computer Program)
Title: Gitksan FST
Author: Clarissa Forbes
Abstract: A finite-state morphological transducer for the Gitksan language (Tsimshianic, BC) implemented in foma. Lexical items are primarily drawn from Hindle & Rigsby (1975).
Year: 2021
Primary URL: https://github.com/caforbes/git_fst
Primary URL Description: Github source code
Access Model: open source program
Programming Language/Platform: foma, python
Source Available?: Yes

An FST morphological analyzer for the Gitksan language (Conference Paper/Presentation)
Title: An FST morphological analyzer for the Gitksan language
Author: Clarissa Forbes
Author: Garrett Nicolai
Author: Miikka Silfverberg
Abstract: This paper presents a finite-state morphological analyzer for the Gitksan language. The analyzer draws from a 1250-token Eastern dialect wordlist. It is based on finite-state technology and additionally includes two extensions which can provide analyses for out-of-vocabulary words: rules for generating predictable dialect variants, and a neural guesser component. The pre-neural analyzer, tested against interlinear-annotated texts from multiple dialects, achieves coverage of (75-81%), and maintains high precision (95-100%). The neural extension improves coverage at the cost of lowered precision.
Date: 08/15/2021
Primary URL: https://sigmorphon.github.io/workshops/2021/2021_SIGMORPHON_Proceedings.pdf
Primary URL Description: Proceedings of SIGMORPHON 2021
Conference Name: SIGMORPHON

An FST morphological analyzer for the Gitksan language. (Conference Paper/Presentation)
Title: An FST morphological analyzer for the Gitksan language.
Author: Clarissa Forbes
Author: Garrett Nicolai
Author: Miikka Silfverberg
Abstract: This paper provides a case study of the ongoing development of a finite-state morphological analyzer for the Gitksan language. The analyzer draws from a 1250-token Eastern dialect wordlist, and was tested against interlinear-annotated texts from multiple dialects. Analyzer coverage varies across dialects (50-70%), but it maintains high precision (95-100%).
Date: 06/11/2021
Conference Name: Americas NLP


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