@inproceedings{halbout:26028:sign-lang:lrec,
  author    = {Halbout, Julie and Braffort, Annelies and Gouiff{\`e}s, Mich{\`e}le and Fabre, Diandra and Lascar, Julie},
  title     = {Learning to Spot Signs from Named Entities. A study on {French} {Sign} {Language}.},
  pages     = {203--211},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion},
  maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Palma, Mallorca, Spain},
  day       = {16},
  month     = may,
  year      = {2026},
  isbn      = {978-2-493814-82-1},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26028.html},
  abstract  = {French Sign Language (LSF) is a low-resourced language, with few available corpora, most of which being only partially annotated. Previous work on other sign languages has explored automatic sign annotation using subtitles as weak supervision, existing signaries, or mouthing cues. This paper focuses on the corpus Matignon-LSF, by first leveraging lexical token spotting then by studying Named Entities (locations, companies, persons). Accounting for the Named entities enables the automatic detection of 30\{\%} to 100\{\%} more signs per class and improves the spotting of rare signs. In addition, this work provides insights into the signing of named entities and contributes resources for improving LSF-to-French translation models.}
}