@inproceedings{battisti:26034:sign-lang:lrec,
  author    = {Battisti, Alessia and Tissi, Katja and Sidler-Miserez, Sandra and Ebling, Sarah},
  title     = {The {SMILE} {Continuous} {DSGS} {Corpus}: A Resource for Longitudinal Exploration of Continuous {Swiss} {German} {Sign} {Language}},
  pages     = {17--30},
  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/26034.html},
  abstract  = {This paper presents the SMILE Continuous DSGS Corpus, a longitudinal dataset that allows for investigating how hearing adults acquire Swiss German Sign Language as a second language. It includes recordings of sign language learners and native signer controls collected at four points over a period of 18 months and annotated for manual and non-manual components, errors, and sentence-level acceptability. The resource provides high-quality, synchronized video suitable for both linguistic and automatic sign language processing research, for example, supporting studies of interlanguage development and training of automatic sign language recognition models. We present here an exploratory analysis of the learner subcorpus using Bayesian mixed-effects modeling. The corpus and accompanying annotations are available for research purposes under a Creative Commons license (CC BY-NC-SA 4.0).}
}

@inproceedings{battisti:24019:sign-lang:lrec,
  author    = {Battisti, Alessia and Tissi, Katja and Sidler-Miserez, Sandra and Ebling, Sarah},
  title     = {Advancing Annotation for Continuous Data in {Swiss} {German} {Sign} {Language}},
  pages     = {1--12},
  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/24019.html},
  abstract  = {This paper presents a transcription and annotation scheme introduced specifically for L1 and L2 continuous data of Swiss German Sign Language, with potential applicability to other sign languages. The scheme includes a novel way of annotating linguistic errors in L2 data, thereby contributing to a deeper understanding of sign language learning. An initial validation approach is outlined, revealing challenges and underscoring the necessity for a more comprehensive method for validating sign language (learner) data. The paper emphasizes the overarching goal of achieving interoperability among sign language corpora and research groups, particularly in advancing sign language data validation techniques.}
}

@inproceedings{ebling:12010:sign-lang:lrec,
  author    = {Ebling, Sarah and Tissi, Katja and Volk, Martin},
  title     = {Semi-Automatic Annotation of Semantic Relations in a {Swiss} {German} {Sign} {Language} Lexicon},
  pages     = {31--36},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12010.html},
  abstract  = {We propose an approach to semi-automatically obtaining semantic relations in Swiss German Sign Language (Deutschschweizerische Geb{\"a}rdensprache, DSGS). We use a set of keywords including the gloss to represent each sign. We apply GermaNet, a lexicographic reference database for German annotated with semantic relations. The results show that approximately 60{\%} of the semantic relations found for the German keywords associated with 9000 entries of a DSGS lexicon also apply for DSGS. We use the semantic relations to extract sub-types of the same type within the concept of double glossing (Konrad 2011). We were able to extract 53 sub-type pairs.}
}

@inproceedings{ebling-etal-2018-smile:lrec,
  author    = {Ebling, Sarah and Camg{\"o}z, Necati Cihan and Boyes Braem, Penny and Tissi, Katja and Sidler-Miserez, Sandra and Stoll, Stephanie and Hadfield, Simon and Haug, Tobias and Bowden, Richard and Tornay, Sandrine and Razavi, Marzieh and Magimai Doss, Mathew},
  title     = {{SMILE} {S}wiss {G}erman Sign Language Dataset},
  pages     = {4221--4229},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo,  H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu},
  booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Miyazaki, Japan},
  day       = {7--12},
  month     = may,
  year      = {2018},
  isbn      = {979-10-95546-00-9},
  language  = {english},
  url       = {https://aclanthology.org/L18-1666},
  abstract  = {Sign language recognition (SLR) involves identifying the form and meaning of isolated signs or sequences of signs. To our knowledge, the combination of SLR and sign language assessment is novel. The goal of an ongoing three-year project in Switzerland is to pioneer an assessment system for lexical signs of Swiss German Sign Language (Deutschschweizerische Geb{\"a}rdensprache, DSGS) that relies on SLR. The assessment system aims to give adult L2 learners of DSGS feedback on the correctness of the manual parameters (handshape, hand position, location, and movement) of isolated signs they produce. In its initial version, the system will include automatic feedback for a subset of a DSGS vocabulary production test consisting of 100 lexical items. To provide the SLR component of the assessment system with sufficient training samples, a large-scale dataset containing videotaped repeated productions of the 100 items of the vocabulary test with associated transcriptions and annotations was created, consisting of data from 11 adult L1 signers and 19 adult L2 learners of DSGS. This paper introduces the dataset, which will be made available to the research community.}
}