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This paper presents a historical parallel corpus of languages spoken in mainland France. It consists of a collection of versions of the Parable of the Prodigal Son, collected during the 19th century. The paper aims to present the interest of such a corpus, its constitution-through XML/TEI encoding, semi-automatic alignment and projection on linguistic maps-and its potential uses for the study of these low-resource languages.
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Description du processus de développement et d’anonymisation d’un corpus en occitan issu d’un forum en ligne et riche en métadonnées liées à la variation dialectale.
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We introduce ForumOccitania, a new Occitan corpus of posts from an online forum, covering a range of topics and dialects. While some existing datasets for this low-resource language include labels of varieties within the dialect continuum, we go one step further by providing metadata pertaining to sociolinguistic factors of language variation (dialect, geographical location, age, proficiency), extracted from self-declared user profiles. We carry out statistical and qualitative analyses, as well as preliminary experiments on unsupervised dialect identification. Our results show that (i) most of the contents is written in Occitan, with the classical spelling conventions, and by young new speakers, (ii) posts display a strong presence of dialectal features from four major Occitan varieties (Lemosin, Lengadocian, Gascon, Provençau), and (iii) a simple topic modelling approach introduced by Kuparinen and Scherrer (2024) effectively detects salient features of these four varieties, but also reveals finer-grained diatopical variation tendencies.
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Whether or not several Creole languages which developed during the early modern period can be considered genetic descendants of European languages has been the subject of intense debate. This is in large part due to the absence of evidence of intermediate forms. This work introduces a new open corpus, the Moly\'e corpus, which combines stereotypical representations of three kinds of language variation in Europe with early attestations of French-based Creole languages across a period of 400 years. It is intended to facilitate future research on the continuity between contact situations in Europe and Creolophone (former) colonies.
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Automatic Text Recognition (ATR) has become a key component of digital editorial workflows, enabling the conversion of scanned documents into TEI. Beyond text recognition, ATR pipelines rely on Document Layout Analysis (DLA) to segment pages into labeled zones that support document reconstruction. Controlled vocabularies such as SegmOnto have already been widely used for this purpose, notably in projects like Gallicorpora and SETA. This paper presents LADaS (Layout Analysis Dataset with SegmOnto), an extension of SegmOnto designed for deeper alignment with the TEI Guidelines. LADaS introduces a two-level annotation system combining SegmOnto broad layout zones with visually identifiable subzones mapped on the TEI. The paper describes the process of building this controlled vocabulary and its documentation, as well as the associated annotated dataset and a proposed pipeline to convert historical scanned documents into deeper encoded TEI files.
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French minority and regional languages remain under-resourced in digital formats, limiting cultural preservation and computational studies. The COLaF project develops corpora and NLP tools for French and languages spoken in France in synchrony and diachrony. This paper presents its TEI schema, which allows for the representation of linguistic variation across temporal, geographic and sociolinguistic dimensions. Using a combination of <language>, <person> and <place> elements, the schema links text segments to metadata describing speakers, locations and varieties. Glottolog identifiers are used for non-standard languages. This paper advocates for the creation of a non-BCP 47-language attribute in TEI to enable the interoperability and TEI-compliance of corpora working on non-standard languages. The schema supports diverse document types from 17th-century monographs to web forums and has been applied to several corpora.
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We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials across diverse document types (magazines, papers from sciences and humanities, PhD theses, monographs, plays, administrative reports, etc.) sorted into modular subsets. By incorporating content from different periods and genres, it addresses varying layout complexities and historical changes in document structure. The modular design allows domain-specific configurations. We evaluate object detection models on this dataset, examining the impact of input size and subset-based training. Results show that a 1280-pixel input size for YOLO is optimal and that training on subsets generally benefits from incorporating them into a generic model rather than fine-tuning pre-trained weights.
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We introduce an open-access and openly-licensed diachronic dataset for Document Layout Analysis (DLA) targeting heterogeneous historical and contemporary materials 4 . The dataset comprises 8,287 manually annotated pages spanning 1600-2024, covering both digitised and born-digital documents across diverse genres including magazines, academic papers, monographs, plays, and administrative records. Annotations follow a two-level vocabulary derived from SegmOnto, extended to paragraph-level semantic regions consistent with the Text Encoding Initiative (TEI), and provide 45 fine-grained classes over 115,007 instances. The data is organised in modular subsets, enabling domain-specific configurations. We establish baselines using two families of object detectors (YOLOv11 and D-FINE) and study three regimes: in-domain training, label-set simplification, and large-scale pretraining (Objects365). We additionally conduct a cross-dataset transfer study against DocLayNet and M 6 Doc using a unified label mapping and a per-decade temporal evaluation. Results show that models trained on existing modern-centric DLA datasets degrade sharply on pre-20th century material (mean F1 dropping by up to 0.25), while in-domain training on our dataset preserves performance across centuries. This highlights the need for diachronic benchmarks and demonstrates the value of combining CV-based detection with DH-oriented annotation standards.
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Langue
- Créoles (1)
- Multilingue (1)
- Occitan (2)