@inproceedings{mocialov-etal-2020-towards,
    title = "Towards Large-Scale Data Mining for Data-Driven Analysis of Sign Languages",
    author = "Mocialov, Boris  and
      Turner, Graham  and
      Hastie, Helen",
    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",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.signlang-1.24/",
    pages = "145--150",
    language = "eng",
    ISBN = "979-10-95546-54-2",
    abstract = "Access to sign language data is far from adequate. We show that it is possible to collect the data from social networking services such as TikTok, Instagram, and YouTube by applying data filtering to enforce quality standards and by discovering patterns in the filtered data, making it easier to analyse and model. Using our data collection pipeline, we collect and examine the interpretation of songs in both the American Sign Language (ASL) and the Brazilian Sign Language (Libras). We explore their differences and similarities by looking at the co-dependence of the orientation and location phonological parameters."
}