@inproceedings{conte:10024:sign-lang:lrec,
  author    = {Conte, Genny and Santoro, Mirko and Geraci, Carlo and Cardinaletti, Anna},
  title     = {Why are you raising your eyebrows?},
  pages     = {53--56},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10024.html},
  abstract  = {It is widely known that sign languages make an extensive use of non-manual markers (NMM) to transmit linguistic information. Some NMMs are specific to particular constructions (in several Sign Languages, furrowed eyebrows is mostly used to mark wh-questions, while headshake is used to mark negation), others may occur in several unrelated constructions (see eyebrow raising in American sign language). This study presents preliminary results of a quantitative investigation of the distribution of raised eyebrows (re-NMM) in Italian Sign Language (LIS). Re-NMM frequently occurs in spontaneous signing and is used to mark a variety of constructions; therefore re-NMM qualifies as a good candidate for a VARBRUL analysis. In particular, re-NMM may mark 8 different constructions in LIS: yes/no-questions, topics, if-clauses, correlative clauses, focus, contrastive focus, subordinate clauses, and the signer's attitude. Data come from a corpus of LIS and have been analyzed with the ELAN software. Results show an even distribution across the sample for most of the uses of re-NMM. Only two functions turned out to be significantly different: the use of re-NMM as a focus marker and the use of re-NMM as an attitude marker, which are sensitive to age.}
}

@inproceedings{geraci:10023:sign-lang:lrec,
  author    = {Geraci, Carlo and Bayley, Robert and Branchini, Chiara and Cardinaletti, Anna and Cecchetto, Carlo and Donati, Caterina and Giudice, Serena and Mereghetti, Emiliano and Poletti, Fabio and Santoro, Mirko and Zucchi, Sandro},
  title     = {Building a corpus for {Italian} {Sign} {Language}. Methodological issues and some preliminary results},
  pages     = {98--101},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10023.html},
  abstract  = {The aim of this paper is to discuss some methodological issues that emerged during the creation of a corpus of data for Italian Sign Language, LIS. Data were collected from 10 cities spread across the country. 18 signers from each city have been recruited. They are native speakers of LIS or later-exposed to LIS and are divided into 3 age groups (19-38, 39-58, 59-78) of 6 signers each (3 males and 3 females). The methodology of data collection and transcription is similar to that used in previous studies of variation in American Sign Language (Lucas, Bayley {\&} Valli 2001) and Australian Sign Language (Johnston {\&} Schembri 2006), with some differences that we discuss. The corpus consists of various kinds of texts collected with different strategies: free conversation (45 minutes), elicited dialogues (about 5-10 minutes), narration (10 minutes) and a picture-naming task (42 items). For the transcription we adopted the ELAN software (Johnston {\&} Crasborn 2006). Finally, a brief report on some preliminary results is presented.}
}