Maintaining The Environment Using AI in Death Language Era

Authors

  • Julhelmi Erlanda STAI UISU Sumatera Utara, Indonesia

DOI:

https://doi.org/10.30983/mj.v3i1.6295

Keywords:

artificial intelligence, ecodigital, ecolinguistic, language revitalization

Abstract

AbstractLocal language cultures that are rich in natural knowledge are becoming extinct. The extinction of these languages certainly increases the threat to the ecosystem. As a language model, AI can create new language trends that support language extinction. However, the author also provides a series of concrete steps to use AI as a language digitization effort. With an ecolinguistic-ecodigital approach and through computational studies of language, AI is expected to contribute to language rescue. With these language revitalization steps, human-human language interactions in the community, humans with nature are better facilitated. The interactional relationship is considered to ensure environmental sustainability.Keywords: artificial intelligence, ecodigital, ecolinguistic, language revitalizationAbstrak Budaya bahasa lokal yang kaya akan pengetahuan alam mulai punah. Kepunahan bahasa-bahasa ini tentu meningkatkan ancaman terhadap ekosistem. Sebagai sebuah model bahasa, AI dapat menciptakan tren bahasa baru yang mendukung kepunahan bahasa. Namun, penulis juga memberikan serangkaian langkah konkret untuk menggunakan AI sebagai upaya digitalisasi bahasa. Dengan pendekatan ekolinguistik-ekodigital dan melalui studi komputasi bahasa, AI diharapkan dapat berkontribusi dalam penyelamatan bahasa. Dengan langkah-langkah revitalisasi bahasa tersebut, interaksi bahasa manusia-manusia di masyarakat, manusia dengan alam menjadi lebih terfasilitasi. Hubungan interaksional tersebut dianggap dapat menjamin kelestarian lingkungan.Kata Kunci: kecerdasan buatan, ekodigital, ekolinguistik, revitalisasi bahasa, linguistik komputasi

References

Bartol, B. (2015). The Web of Technics: Education and Lewis Mumford in the Information Age (Essays of Significance 2015).

Blokland, R., Partanen, N., Rießler, M., & Wilbur, J. (2019). Using Computational Approaches to Integrate Endangered Language Legacy Data into Documentation Corpora: Past Experiences and Challenges Ahead. Proceedings of the Workshop on Computational Methods for Endangered Languages, 2(1). https://doi.org/10.33011/computel.v2i.451

De Graaf, T. (2011). Endangered languages and the use of sound archives and fieldwork data for their documentation and revitalisation: Voices from Tundra and Taiga. International Journal of Asia-Pacific Studies, 7(1), 27–46.

Fostar, J. B. (2021). Like Death but Without Death: the Language-Death-Metaphor and Another Option. Linguaculture, 12(2), 85–101. https://doi.org/10.47743/lincu-2021-2-0200

Hodge, G., & Goico, S. A. (2022). Natural and elicited: Sign language corpus linguistics and linguistic ethnography as complementary methodologies. Journal of Sociolinguistics, 26(1), 126–136. https://doi.org/10.1111/josl.12523

King, K. A. (2023). Challenges in doing research to support language revitalization aims. Linguistic Approaches to Bilingualism, 13(1), 56–59. https://doi.org/10.1075/lab.22056.kin

Lane, P., Hagen, K., Nøklestad, A., & Priestley, J. (2022). Creating a corpus for Kven, a minority language in Norway. Nordlyd, 46(1), 159–170. https://doi.org/10.7557/12.6345

Low, D. S., Mcneill, S., & Day, M. J. (2022). Endangered Languages: A Sociocognitive Approach To Language Death, Identity Loss, And Preservation In The Age Of Artificial Intelligence. Sciendo, Sustainabl, 1–23. https://doi.org/https://doi.org/10.2478/sm-2022-0011

Meighan, P. J. (n.d.). Respecting the Territory: Self-Determined and Relational Technology in Indigenous Language Revitalization. 17(2021), 397–405.

Mirza, A., & Sundaram, D. (2017). Collective Intelligence based Endangered Language Revitalisation Systems: Design, Implementation, and Evaluation. EAI Endorsed Transactions on Context-Aware Systems and Applications, 4(11), 152338. https://doi.org/10.4108/eai.6-3-2017.152338

Shibata, H., Miki, S., & Nakamura, Y. (2023). Playing the Werewolf game with artificial intelligence for language understanding. 1–14.

Suri, K., Singh, A., Mishra, P., Rout, S. S., & Sabapathy, R. (2023). Language Models sounds the Death Knell of Knowledge Graphs. Computer Science, Computation and Language.

Turin, M. (2012). Voices of vanishing worlds: Endangered languages, orality, and cognition. Analise Social, 47(4), 846–869.

Yang, H. (2022). The current research trend of artificial intelligence in language learning : A systematic empirical literature review from an activity theory perspective. Australasian Journal of Educational Technology, 38(5), 180–210.

Yuspita, Y. E., Minova, P. N., & Ansara, A. D. P. (2022). Selection Of Internet Provider To Improve Quality Of Service And Learning Using Decision Support System. Jurnal Mantik, 6(1), 105–111.

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Published

2023-06-30

How to Cite

Erlanda, J. (2023). Maintaining The Environment Using AI in Death Language Era. Modality Journal: International Journal of Linguistics and Literature, 3(1), 71–78. https://doi.org/10.30983/mj.v3i1.6295

Issue

Vol. 3 No. 1 (2023): June 2023

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Articles

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