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Cet article propose une réflexion sur les défis de la documentation des langues minorisées dans l’espace numérique à partir des travaux réalisés dans le cadre du projet DIVITAL. Les premiers travaux du projet ont concerné la collecte de corpus et leur documentation par des métadonnées à grain fin. Ces travaux ont mis en évidence deux défis majeurs : (i) l’identification des langues et de leurs variantes, dans le cadre des normes de codification des noms de langues, et (ii) la création de nouvelles ressources en lien avec les pratiques actuelles de ces langues. , This article looks at the challenges of documenting minority languages in the digital environment, based on work carried out as part of the DIVITAL project. The project’s initial work involved collecting corpora and documenting them using fine-grained metadata. This work has highlighted two major challenges: (i) the identification of languages and their variants, within the framework of standards for the codification of language names, and (ii) the creation of new resources linked to the current practices of these languages. , Dieser Artikel stellt Überlegungen zu den Herausforderungen der Dokumentation von Minderheitensprachen im digitalen Raum an, ausgehend von den Arbeiten, die im Rahmen des DIVITAL-Projekts durchgeführt wurden. Die ersten Arbeiten des Projekts betrafen die Sammlung von Korpora und ihre Dokumentation durch feinkörnige Metadaten. Diese Arbeiten haben zwei große Herausforderungen aufgezeigt: (i) die Identifizierung der Sprachen und ihrer Varianten im Rahmen der Normen für die Kodierung von Sprachnamen und (ii) die Schaffung neuer Ressourcen in Verbindung mit der aktuellen Praxis dieser Sprachen.
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We investigate the effect of integrating lexicon information to an extremely low-resource language when annotated data is scarce for morpho-syntactic analysis. Obtaining such data and linguistic resources for these languages are usually constrained by a lack of human and financial resources making this task particularly challenging. In this paper, we describe the collection and leverage of a bilingual lexicon for Poitevin-Saintongeais, a regional language of France, to create augmented data through a neighbor-based distributional method. We assess this lexicon-driven approach in improving POS tagging while using different lexicon and augmented data sizes. To evaluate this strategy, we compare two distinct paradigms: neural networks, which typically require extensive data, and a conventional probabilistic approach, in which a lexicon is instrumental in its performance. Our findings reveal that the lexicon is a valuable asset for all models, but in particular for neural, demonstrating an enhanced generalization across diverse classes without requiring an extensive lexicon size.
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Parallel corpora are still scarce for most of the world's language pairs. The situation is by no means different for regional languages of France. In addition, adequate web interfaces facilitate and encourage the use of parallel corpora by target users, such as language learners and teachers, as well as linguists. In this paper, we describe ParCoLab, a parallel corpus and a web platform for querying the corpus. From its onset, ParCoLab has been geared towards lower-resource languages, with an initial corpus in Serbian, along with French and English (later Spanish). We focus here on the extension of ParCoLab with a parallel corpus for four regional languages of France: Alsatian, Corsican, Occitan and Poitevin-Saintongeais. In particular, we detail criteria for choosing texts and issues related to their collection. The new parallel corpus contains more than 20k tokens per regional language.
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Langue
- Poitevin-Saintongeais
- Alsacien (2)
- Corse (2)
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Multilingue
(2)
- Langues COLaF (2)
- Occitan (2)
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- Projet (1)