@inproceedings{rathmann:24002:sign-lang:lrec,
  author    = {Rathmann, Christian and Quadros, Ronice M{\"u}ller de and Gei{\ss}ler, Thomas and Peters, Christian and Fernandes, Francisco and Loio, Milene Peixer and Fran{\c c}a, Diego},
  title     = {{VisuoLab}: Building a sign language multilingual, multimodal and multifunctional platform},
  pages     = {282--289},
  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/24002.html},
  abstract  = {VisuoLab is a multifunctional, multimodal and multilingual platform designed for sign language communities. This platform is based on web accessibility and usability, and is specifically designed in a visual way. All resources are organized to be available in sign languages and written languages for different purposes: to provide materials related to and in sign languages, to produce materials (such as papers, video books, teaching materials that include signing production), to teach with signing tools, to interpret and translate activities for training purposes, and to evaluate signing progress. VisuoLab is designed as an open source platform. The current stage of VisuoLab is a beta version available in the development area of Levante Lab for the platform: https://visuolab.levantelab.com.br/}
}

@inproceedings{borstell:20011:sign-lang:lrec,
  author    = {B{\"o}rstell, Carl and Crasborn, Onno and Whynot, Lori},
  title     = {Measuring Lexical Similarity across Sign Languages in {Global} {Signbank}},
  pages     = {21--26},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2020} 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives},
  maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {16},
  month     = may,
  year      = {2020},
  isbn      = {979-10-95546-54-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20011.html},
  abstract  = {Lexicostatistics is the main method used in previous work measuring linguistic distances between sign languages. As a method, it disregards any possible structural/grammatical similarity, instead focusing exclusively on lexical items, but it is time consuming as it requires some comparable phonological coding (i.e. form description) as well as concept matching (i.e. meaning description) of signs across the sign languages to be compared. In this paper, we present a novel approach for measuring lexical similarity across any two sign languages using the Global Signbank platform, a lexical database of uniformly coded signs. The method involves a feature-by-feature comparison of all matched phonological features. This method can be used in two distinct ways: 1) automatically comparing the amount of lexical overlap between two sign languages (with a more detailed feature-description than previous lexicostatistical methods); 2) finding exact form-matches across languages that are either matched or mismatched in meaning (i.e. true or false friends). We show the feasability of this method by comparing three languages (datasets) in Global Signbank, and are currently expanding both the size of these three as well as the total number of datasets.}
}