@inproceedings{delagarza:26039:sign-lang:lrec,
  author    = {de la Garza, Lorena and Halbout, Julie and Lascar, Julie and Mart{\'i}nez-Guevara, Niels and Curiel, Arturo and Gouiff{\`e}s, Mich{\`e}le and Braffort, Annelies},
  title     = {Extracting Signs from Weakly Aligned Sign Language Corpora: A Study on {LSF} and {LSM}},
  pages     = {174--183},
  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 = {{ELRA 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/26039.html},
  doi       = {10.63317/38kfot52b4dz},
  abstract  = {This paper presents a framework for the automatic annotation of sign language data across different recording conditions, including original and interpreted content. The proposed approach integrates weak alignment, sign segmentation, and multiple instance learning with a contrastive loss. The resulting annotations are subsequently refined and filtered to enhance their reliability. Our method was applied to two historically related sign languages, French Sign Language (LSF) and Mexican Sign Language (LSM). This led to the creation of two signaries, comprising approximately 2k categories in LSF (25k occurrences) and 41 categories in LSM (1k occurrences). Both resources provide valuable support for future research in artificial intelligence and linguistics, particularly for comparative analyses between the two languages. A seminal analysis is presented as part of this paper.}
}

@inproceedings{martinezguevara:24027:sign-lang:lrec,
  author    = {Mart{\'i}nez-Guevara, Niels and Curiel, Arturo},
  title     = {Quantitative Analysis of Hand Locations in both Sign Language and Non-linguistic Gesture Videos},
  pages     = {225--234},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24027.html},
  doi       = {10.63317/4kupnuhhgusr},
  abstract  = {This paper explores whether measurable quantitative linguistic relationships are readily apparent in the use of space of three different Sign Languages (SLs): British Sign Language (BSL), Dutch Sign Language (NGT) and Mexican Sign Language (LSM). To this end, three SL datasets were collected; one for each of the languages of interest. Informative video frames were extracted from the collected datasets, which in turn were automatically processed to detect hand locations. The obtained information was analyzed through statistical methods, and compared against a dataset of non-linguistic gestural communication: the latter, in an effort to observe whether space-use differs between linguistic and non-linguistic gestures. The results show that meaningful gestures---regardless of whether they are deemed linguistic or not---seem to induce a spatial hierarchy around the gesturer, disproportionately favoring certain areas during articulation. SLs in particular seem to exert pressure on those areas to become more efficient, as signers appear to concentrate hand activity over more cohesive regions than non-signers. In addition, these results point towards an indirect relationship between culturally-recognized gestures and their surrounding SLs, showing that there is still work to be done on the exploration of iconicity and its effects on gestural communication.}
}