Dejan Stosic


2024

pdf
The ParCoLab Parallel Corpus and Its Extension to Four Regional Languages of France
Dejan Stosic | Saša Marjanović | Delphine Bernhard | Myriam Bras | Laurent Kevers | Stella Retali-Medori | Marianne Vergez-Couret | Carole Werner
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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.

2018

pdf bib
De la constitution d’un corpus arboré à l’analyse syntaxique du serbe [From the constitution of a treebank to the syntactic analysis of the Serbian language]
Aleksandra Miletic | Cécile Fabre | Dejan Stosic
Traitement Automatique des Langues, Volume 59, Numéro 3 : Traitement automatique des langues peu dotées [NLP for Under-Resourced Languages]

2016

pdf
Mise au point d’une méthode d’annotation morphosyntaxique fine du serbe (Developping a method for detailed morphosyntactic tagging of Serbian)
Aleksandra Miletic | Cécile Fabre | Dejan Stosic
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)

Cet article présente une expérience d’annotation morphosyntaxique fine du volet serbe du corpus parallèle ParCoLab (corpus serbe-français-anglais). Elle a consisté à enrichir une annotation existante en parties du discours avec des traits morphosyntaxiques fins, afin de préparer une étape ultérieure de parsing. Nous avons comparé trois approches : 1) annotation manuelle ; 2) préannotation avec un étiqueteur entraîné sur le croate suivie d’une correction manuelle ; 3) réentraînement de l’outil sur un petit échantillon validé du corpus, suivi de l’annotation automatique et de la correction manuelle. Le modèle croate maintient une stabilité globale en passant au serbe, mais les différences entre les deux jeux d’étiquettes exigent des interventions manuelles importantes. Le modèle ré-entraîné sur un échantillon de taille limité (20K tokens) atteint la même exactitude que le modèle existant et le gain de temps observé montre que cette méthode optimise la phase de correction.

2014

pdf
TALC-sef A Manually-Revised POS-TAgged Literary Corpus in Serbian, English and French
Antonio Balvet | Dejan Stosic | Aleksandra Miletic
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we present a parallel literary corpus for Serbian, English and French, the TALC-sef corpus. The corpus includes a manually-revised pos-tagged reference Serbian corpus of over 150,000 words. The initial objective was to devise a reference parallel corpus in the three languages, both for literary and linguistic studies. The French and English sub-corpora had been pos-tagged from the onset, using TreeTagger (Schmid, 1994), but the corpus lacked, until now, a tagged version of the Serbian sub-corpus. Here, we present the original parallel literary corpus, then we address issues related to pos-tagging a large collection of Serbian text: from the conception of an appropriate tagset for Serbian, to the choice of an automatic pos-tagger adapted to the task, and then to some quantitative and qualitative results. We then move on to a discussion of perspectives in the near future for further annotations of the whole parallel corpus.