Notice bibliographique
Résumé
We describe three ongoing projects involving different First Peoples’ languages of Canada (Cree/nehiyawewin, Dene Sųłiné, and Nakoda/Stoney) that centre around the recording, transcription, compilation, and analysis of spontaneous oral language use––some narrative, some conversation––using freely available, Unicode-savvy corpus software (in this case, AntConc [Anthony 2014]) and little to no up- front annotation or translation into English. Because these languages are all polysynthetic, lemmatization and POS tagging are either unachievable or excessively time-draining and indeterminate activities. Nevertheless, corpus creation can still continue apace and reap huge benefits using the most basic of corpus tools. These projects are consonant with a growing ethos in language documentation circles that advocate for the value of corpus development alongside more traditional documentary activities (cf. McEnery & Ostler 2000, Woodbury 2003, Crowley 2007, Cox 2011, Mosel 2014, Vinogradov 2016). Each corpus is at a different stage of development, yet we hope to persuade community-based colleagues of the enormous benefits that ensue from the deliberate creation and use of a corpus of naturally occurring language data for language analysis and teaching. Direct benefits include ready-to-hand word lists; authentic sample utterances for exemplifying dictionaries, phrasebooks, and grammatical sketches; and a conscientious focus on recording many speakers across different demographic categories, discursive situations, and registers in order to achieve a broad range of usage conditions. A focus on wide and balanced sampling clearly strengthens the data pool from which analyses can follow. But it also results in a closer connection by speakers/learners to important and recurring phenomena in their language rather than to descriptions of phenomena that may have emerged through bilingual situations with a handful of speakers under the direct control of non-speaking linguists (who may have been guided by theoretical concerns unrelated to actual language use). Our demonstration corpora vary in size and composition, but each is already useful in revealing frequency, collocational, and distributional information about lexical items and morphosyntactic devices that may have received scant prior attention. We discuss the basics of corpus creation from scratch, the role of strategic metadata and file-naming practices, and illustrate the types of immediately interpretable analyses that standard corpus tools can provide with monolingual, untagged transcripts. Best of all, once the central principles and logistics of corpus creation are mastered, the corpus can grow in a natural and incremental way, involving an expanding group of participants. Ultimately, a broadly sampled corpus can provide a solid empirical basis for the study of lexico-syntactic phenomena, not to mention a lasting, reusable, and shareable record of actual language use. References Anthony, L. 2014. AntConc (Version 3.4.1m) [Computer Software]. Tokyo: Waseda University. Available from http://www.laurenceanthony.net/. Cox, C. 2011. Corpus linguistics and language documentation: Challenges for collaboration. In Newman, J., R. H. Baayen, & S. Rice (eds.), Corpus-Based Studies in Language Use, Language Learning, and Language Documentation, 239-264. Amsterdam: Brill. Crowley, T. 2007. Field Linguistics: A Beginner’s Guide. Oxford: Oxford University Press. McEnery, T. & N. Ostler. 2000. A new agenda for corpus linguistics––working with all of the world’s languages. Literary and Linguistic Computing 15 (4): 403-420. Mosel, U. 2014. Corpus linguistic and documentary approaches in writing a grammar of a previously undescribed language. Language Documentation and Conservation 8: 135-157. Vinogradov, I. 2016. Linguistic corpora of understudied languages: Do they make sense? Káñina 40(1): 127-141. Woodbury, T. 2003. Defining documentary linguistics. Language Documentation and Description 1(1): 35-51.
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 | 0,024 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,016 | 0,008 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,006 | 0,011 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».