@inproceedings{meurant:16032:sign-lang:lrec,
  author    = {Meurant, Laurence and Cleve, Anthony and Crasborn, Onno},
  title     = {Using sign language corpora as bilingual corpora for data mining: Contrastive linguistics and computer-assisted annotation},
  pages     = {159--166},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining},
  maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Portoro{\v z}, Slovenia},
  day       = {28},
  month     = may,
  year      = {2016},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16032.html},
  abstract  = {More and more sign languages nowadays are now documented by large-scale digital corpora. But exploiting sign language (SL) corpus data remains subject to the time consuming and expensive manual task of annotating. In this paper, we present an ongoing research that aims at testing a new approach to better mine SL data. It relies on the methodology of corpus-based contrastive linguistics, exploiting SL corpora as bilingual corpora. We present and illustrate the main improvements we foresee in developing such an approach: downstream, for the benefit of the linguistic description and the bilingual (signed - spoken) competence of teachers, learners and the users; and upstream, in order to enable the automatisation of the annotation process of sign language data. We also describe the methodology we are using to develop a concordancer able to turn SL corpora into searchable translation corpora, and to derive from it a tool support to annotation.}
}

@inproceedings{meurant-etal-2016-modelling:lrec,
  author    = {Meurant, Laurence and Gobert, Maxime and Cleve, Anthony},
  title     = {Modelling a Parallel Corpus of {F}rench and {F}rench {B}elgian {S}ign {L}anguage},
  pages     = {4236--4240},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Goggi, Sara and Grobelnik, Marko and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  booktitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Portoro{\v z}, Slovenia},
  day       = {23--28},
  month     = may,
  year      = {2016},
  isbn      = {978-2-9517408-9-1},
  language  = {english},
  url       = {https://aclanthology.org/L16-1670},
  doi       = {10.63317/4wb9eoht5a48},
  abstract  = {The overarching objective underlying this research is to develop an online tool, based on a parallel corpus of French Belgian Sign Language (LSFB) and written Belgian French. This tool is aimed to assist various set of tasks related to the comparison of LSFB and French, to the benefit of general users as well as teachers in bilingual schools, translators and interpreters, as well as linguists. These tasks include (1) the comprehension of LSFB or French texts, (2) the production of LSFB or French texts, (3) the translation between LSFB and French in both directions and (4) the contrastive analysis of these languages. The first step of investigation aims at creating an unidirectional French-LSFB concordancer, able to align a one- or multiple-word expression from the French translated text with its corresponding expressions in the videotaped LSFB productions. We aim at testing the efficiency of this concordancer for the extraction of a dictionary of meanings in context. In this paper, we will present the modelling of the different data sources at our disposal and specifically the way they interact with one another.}
}