Editorial for the Special Issue on Computational Linguistics Processing in Low-Resource Indigenous Languages
Notice bibliographique
Résumé
Many of the indigenous languages today are struggling to survive, and they are in danger of disappearing.Closely followed by Africa, Asia has the most indigenous languages.Though indigenous languages have many sources in existence from where we can obtain acceptable knowledge, mythology, history, and perception of their communities, their diversity is decreasing at an alarming rate due to social pressure, external forces, and demographic changes.The systematic disappearance of indigenous languages threatens the lives of millions of families, children, and indigenous communities as well as the survival of their languages worldwide.Most indigenous languages have no written form; which makes them difficult to process and analyze using computational models.However, these languages need to be preserved as they are rich in oral traditions, and they remain remarkably consistent and reliable over time.Presently, many researchers and scientists are actively finding more evidence on indigenous languages to create language processing models using a variety of techniques.Exploring more in terms of grammar, words, and unique rules of sound help us understand the language intuitively and create more efficient linguistic models.However, this process generally tends to be more complex as these languages have very few resources and are often spoken in remote areas by fewer people.Computational linguistics applies computer science techniques for the analysis and synthesis of written and spoken languages.The practical goal of using computational linguistics for indigenous languages is comprehensive.It helps formulate semantic and grammatical frameworks for distinguishing languages through the computationally manageable implementation of semantic and syntactic analysis.Hence, the discovery of more advanced computational linguistics processing algorithms and learning principles that can effectively use the structural and distributional properties of indigenous languages is crucial.It helps develop cognitively and neuroscientifically reasonable computational models that work in the same way that indigenous language processing and learning might occur in the brain.This special issue was dedicated to explain how computational linguistics and natural language processing algorithms make inferences and gain insights into existing data of low-resource indigenous languages.The content mainly focuses on innovative research that formalizes human communication and spoken indigenous languages into the computational system.We welcomed researchers and practitioners from industry and academia to present their contributions against this background.This special issue saw a total of 21 submissions, from which five papers were published.It was intentional to adhere to a strict acceptance rate and ensure that only the best papers in the scope of
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,118 | 0,062 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».