@inproceedings{filhol:26045:sign-lang:lrec,
  author    = {Filhol, Michael and Martinod, Emmanuella},
  title     = {Formalising Sign Language Depiction, Characterising Categories and Measuring Iconicity with {AZee}},
  pages     = {164--173},
  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/26045.html},
  abstract  = {This paper deals with depiction in (French) Sign Language, the formal account AZee can provide, and how it compares, validates or simplifies the linguistic notions of classifiers and iconic structures. It reports on a partial encoding work on "Mocap1", a corpus with a high density of depicting structures, following the same method that led to the first AZee reference corpus "40 br{\`e}ves". The approach does not postulate classifiers or iconic structures as entities separate from lexical signs, and nonetheless manages to model the corpus data. We discuss the entailed possibility to rediscover some of the useful categories, and if so define them from AZee's premises. We also specify how a formal metric can be specified to measure iconicity in signed data. While this paper is of linguistic interest as it compares to existing theories, it also provides a concrete step to covering depicting discourse with AZee, therefore enable automatic SL animation of depiction.}
}