Cleve, Anthony

Gobert, Maxime

Meurant, Laurence

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

Repository of the University of Namur

RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHEAuthor(s) - Auteur(s) :Publication date - Date de publication :Permanent link - Permalien :Rights / License - Licence de droit d’auteur :Bibliothèque Universitaire Moretus PlantinInstitutional Repository - Research PortalDépôt Institutionnel - Portail de la Rechercheresearchportal.unamur.beModelling a parallel corpus of French and French Belgian Sign Language (LSFB)Meurant, Laurence; Gobert, Maxime; Cleve, AnthonyPublished in:Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016Publication date:2016Document VersionPeer reviewed versionLink to publicationCitation for pulished version (HARVARD):Meurant, L, Gobert, M & Cleve, A 2016, Modelling a parallel corpus of French and French Belgian SignL guage (LSFB). in Proceedings of the 10th International Conference on Language Resources and Evaluation,LREC 2016. European Language Resources Association (ELRA), pp. 4236-4240, Portoroz, Slovenia, 23/05/16.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 21. May. 2019Towards a corpus-based online tool for French - Sign Language (LSFB) needs The overarching objective underlying this proposal is to develop an online tool, based on a parallel corpus of French Belgian Sign Language and written French data, and aimed to assist a various set of tasks related to the comparison of LSFB and French. 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 comparative description of these languages. To the best of our knowledge, this kind of tool has never been created before.  The first step of investigation aims at creating a (unidirectional) French-LSFB (then, written-video) concordancer associated with a colocation calculator and to test the efficiency of this concordancer for the extraction of a dictionary of meanings in context.  In this proposal, we will present the modelling of the different data sources at our disposal and specifically the way they interact with one another.  Linguistic resources and Sign languages Sign languages (SLs) are among the less-resourced languages of the world, due to the combined impact of various factors including their status of minority languages, the lack of written form of these visual-gestural languages, and their only recent official acceptance and recognition in the society. Research in SL linguistics is generally considered to have begun with the works of Tervoort (1953) and Stokoe (1960). The foundation of the Sign Language Linguistics Society in 2004 symbolized that research on SLs had become a worldwide effort. The digital revolution has had an important impact on the knowledge of Sign Languages since it opened (in the early 2000s only)  the possibility to develop Corpus linguistics on sign languages by collecting, archiving, annotating and documenting large scale videotaped data (Johnston, 2010). Nowadays, annotating SL data remains a manual and time-consuming task. But this slow process is unavoidable at this stage in order to enlarge the available data needed to automate the process in a short future. We hypothesize that building our bilingual tool will contribute to this movement towards annotation automation.  In 2012 started in the University of Namur the “Corpus LSFB” project. The project will be published (online and open access) at the end of 2015. If this resource is essential to the linguistic description of LSFB, it is a potential wealth of information for pedagogic purposes, for the field of translation and interpretation studies and for the field of contrastive linguistics between signed and spoken languages.   From Sign language corpora to Sign-spoken languages tools Beyond the domain of SL linguistics, the computer revolution also impacted the domain of contrastive linguistics in general by having allowed the development of multilingual corpora. Multilingual corpora, combined with alignment and search tools, are today acknowledged for their theoretical as well as practical importance in cross-linguistic studies and applications: they provide a rich basis of languages correspondences in context that can serve as testbeds for linguistic theories and hypotheses, but they are also essential for applications in the fields of lexicography, natural language processing, automatic or machine-assisted translation and language teaching (Altenberg et al. 2002, Johansson, 2007). Multilingual corpora are the basis of all multilingual concordancer as TransSearch (Bourdaillet et al. 2010) or Linguee (Linguee 2015). Due to the visual-gestural nature of SLs, most modern SL machine-readable corpora, as the Corpus LSFB is, are multimodal corpora, since the videotaped data are accompanied by the written glosses of the signs and by the translation of the videos in written language. But as far as we know, this property of SL corpora has not been exploited yet for the development of bilingual tools. On the one hand, sign language engineering is mostly devoted to automatic or assisted translation tools (e.g. the ‘SignSpeak’ project1; Filhol et al., 2014; Dreuw et al., 2010; Morrissey et al., 2005), or for SL recognition (e.g. the ‘Dicta Sign’ project2; Dreuw et al., 2008). On the other hand, as SL corpora are recent and their number is still small, corpus-based SL dictionaries are scarce (the German Sign Language (DGS) dictionary in preparation3 is an exception).  The innovation of this research lies in making a SL corpus, i.e. the Corpus LSFB, an aligned and searchable translation corpus  (namely LSFB productions translated by human translators into French), providing aligned bilingual examples of words and signs in context, at the service of language teaching, translation, and contrastive linguistics. An overview of the possible user interface of the tool is shown in Figure 1 (the terms between asterisks refer to the entities’ names used in Figure 2).                                                  1 http://www.signspeak.eu/ 2 http://www.dictasign.eu/ 3 http://www.sign-lang.uni-hamburg.de/dgs-korpus/index.php/dictionary.html  Figure 1 : Model of the possible user interface based on fictive examples (inspired by the Linguee user interface). The terms between asterisks refer to the entities’ names used in Figure 2  Available resources  Data and tools at our disposal are: ●   LSFB data: 125 hours of HD, 50 f/sec. videos from “the Corpus LSFB”, containing semi-directed spontaneous productions of 50 pairs of signers from Brussels and all regions of Wallonia. The productions are elicited by a systematic list of 18 tasks guided by a deaf moderator and covering a variety of genres (narratives, conversations, explanations, argumentations and descriptions) and topics.  ●   LSFB annotations: 10 out of these 125 hours of videos are glossed, which means that each sign is given an ID-Gloss (Johnston, 2010), namely a written label of the lemma corresponding to the sign token (e.g. the ID-Gloss “PENSER” (‘think’) for all the possible forms of the verb). The annotation is made in ELAN4, which allows creating and searching annotations aligned with video sources. The annotation files are associated and time-aligned with the corresponding videos. ●   Translations: 5 out of the 10 hours of annotated data have a free translation in French. ●   Lex-LSFB: all the ID-Glosses entered in the annotation files (currently 2500 entries) are collected within an online, constantly evolving lexical database: Lex-LSFB. Each entry of Lex-LSFB includes the ID-Gloss of the sign, one or several possible translations of the sign in French, an animated GIF file showing the sign in isolation, and information about the variants of the sign. Lex-LSFB and ELAN are connected: each entry of Lex-LSFB is linked to the various occurrences of the sign in the videos. External tools and related data resources are also available: • CoBRA (Corpus Based Reading Assistant) is an online and interactive tool developed at University of Namur (Deville et al., 2013), based on bilingual corpora (Dutch-French and English-French) aligned at the level of the sentence, that allows the teachers to create labelled texts in Dutch (NL) or in English (EN) and French-speaking learners to be assisted in their reading by clicking on any word in order to know its meaning in its particular context of occurrence. The translation of each term is illustrated by a series of bilingual citations extracted from bilingual corpora. CoBRA is based on a searchable concordancer, called the “Dico Corpus” tool, and on two bilingual dictionaries (FR-NL and FR-EN) called “DiCoBRA” that are (1) produced from a contrastive approach of the existing dictionaries of each language and (2) completed by the contrastive data provided by “Dico Corpus”.   • CoBRA Corpus: the CoBRA resources currently include a global text corpus of over 30.000.000 words among which circa 15.000.000 French words, about 10.000.000 concordances (i.e.                                                 4 http://tla.mpi.nl/tools/tla-tools/elan/ aligned bilingual examples), an English-French glossary of about 19.000 entries, and a Dutch-French glossary of about 20.000 entries. • DiCoBRA: CoBRA’s dictionary includes circa 87.000 lemma and 300.000 inflected forms of French.  Modelling Corpus-LSFB data resources The first step of this project consisted in formally modelling the various data artifacts involved in the Corpus-LSFB and its environment. Figure 2 provides a simplified “helicopter-view” of this data model, by means of an Entity-Relationship (ER) diagram. This model represents the main concepts involved in the corpus, as well as their characteristics and relationships. Figure 2 : Simplified Entity-Relationship diagram of the data model The Corpus-LSFB consists of a set of videos (LSFB_VIDEO) where two signers achieved a task in LSFB. Each video is identified by a unique id, corresponding to its Unique Resource Identifier (uri), and is characterised by the duration of the video (Duration), and a brief description of the task (Task_Description).  The corpus also includes a large set of LSFB signs (LSFB), characterised by a unique id gloss (ID_Gloss). Each LFSB sign in the corpus is linked to a set of french keywords (FR_KEYWORD)  that represent the different possible meanings of the LSFB sign.  The occurrence of a given LSFB sign in a video is represented through entity type SIGN_ANNOTATION. An annotation indicates the exact time period during which the sign appears in the video, in the form of a time interval (Begin and End). Note that when the same sign S occurs N times in the very same video V, there are N annotations linking S and V in the corpus, each with a distinct time interval. The annotation also records which of the two signers is the author of the sign, via attribute Turn. By convention, the value of attribute Turn is either ‘A’ (signer A) or ‘B’ (signer B).  The corpus also provides, for a subset of the LSFB videos, the full French translation (FR_TRANSLATION) of the task. Each translation is made up of a set of French translation fragments (FR_TRANSLATION_FRAGMENT), that is a French text fragment (Text) translating what is expressed in LSFB by one of the two persons (Turn) during time interval [Begin, End] of the video. An external text corpus gracefully complements the Corpus-LSFB. This corpus consists of a large set of French texts, available through the CoBRA toolsuite. Those texts are in turn composed of French sentences (FR_SENTENCE), where contextual occurrences of each FR_KEYWORD (or one of its inflected forms) may possibly be found.    Populating the Corpus-LSFB data model Once the various Corpus-LSFB data artifacts have been modelled in a conceptual way, an important and challenging task remains to be done: populating the data mode in order to eventually build a queryable Corpus-LSFB database. At the time of writing this abstract, this task has almost been achieved in the sense that most data artifacts are currently recorded and/or referenced in the ELAN tool (LSFB_VIDEO, SIGN_ANNOTATION, FR_TRANSLATION and FR_TRANSLATION_FRAGMENT) and in the Lex-LSFB database (LSFB_SIGN and FR_KEYWORD). Both environments are connected to each other since the ELAN annotations make use of the same ID_Gloss values of the Lex-LSFB database. The external resources are mainly available through the CoBRA toolsuite, the database of which allows one to recover the different contexts of occurrence (FR_SENTENCE) of a French keyword (FR_KEYWORD), or of one of its inflected form, in a large aligned corpus of French texts (FR_TEXT).  Exploiting the Corpus-LSFB populated data model Once a complete and queryable Corpus-LSFB database will be available, we anticipate several usage scenarios of this database in the short term, as a first stage of the overall bilingual tool that we want to develop in the longer term.  First, we will need to develop a tool allowing us to further align (at the level of the French translation fragment) both sides of the Corpus LSFB, namely the annotated videos of LSFB productions and their French textual translations. The challenge of this task will be to automatically relate each sign annotation (i.e., an ID_Gloss and a [begin,end] time interval) to the corresponding part of the French translation fragment where the meaning of this sign is given in context. The [Begin, End] time interval of the French translation fragments will allow the alignment tool to identify the right fragment (the one that includes the [Begin, End] time interval of the sign). However, since the very same translation fragment may be linked to several (possibly numerous !) successive LSFB signs, the tool will then need to further slice the translation fragment in smaller fragments, each relating to one “clause-like” unit in LSFB. With the help of the alignment tool described above, we will derive a finer-grained Corpus-LSFB database, that we will then use as a basis to build advanced interactive tools and conduct systematic (contrastive) linguistic studies. The first interactive tool we want to build is a searchable concordancer that exploits the aligned, fine-grained Corpus-LSFB database as well as the external corpus in order to find the LSFB concordances related to a given input term in French, as shown in Figure 1.   From a scientific point of view, the very same database can also be exploited for conducting linguistic studies and enrich our knowledge of the contextual usages and meanings of a LSFB sign, but also to compare the ways (lexicon, paraphrase, depicting structures, etc.) French and LSFB express the same ideas.  References Altenberg, B. & Granger, S., eds. Lexis in contrast: corpus-based approaches. Vol. 7. John Benjamins Publishing, 2002. Bourdaillet, J., Huet, S., Langlais, P., & Lapalme, G. TransSearch: from a bilingual concordancer to a translation finder. Machine Translation, 24(3-4), 241-271, 2010. Deville, G., Dumortier, L., Meurisse, J-R. & Miceli, M. Ressources lexicales pour l’aide à l’apprentissage des langues In Gala, N. & Zock, M. (eds). Ressources Lexicales: Contenu, construction, utilisation, évaluation. John Benjamins, Vol 30 , p. 291-312, 2013. Dreuw, P., Ney, H., Pérez, G. M., Crasborn, O., Piater, J. H., Moya, J. M., & Wheatley, M. The SignSpeak Project-Bridging the Gap Between Signers and Speakers. In LREC, 2010. Dreuw, P., Neidle, C., Athitsos, V., Sclaroff, S., & Ney, H. Benchmark Databases for Video-Based Automatic Sign Language Recognition. In LREC, 2008. M. Filhol & Tannier, X. Construction of a French-LSF corpus, Building and Using Comparable Corpora. In LREC, 2014. Johnston, T. From archive to corpus: transcription and annotation in the creation of signed language corpora. International Journal of Corpus Linguistics, 15(1), 106-131, 2010. Johansson, Stig. Seeing through multilingual corpora. Language and Computers 62.1 (2007): 51-71. Linguee, http://www.linguee.com/, last visit 25/10/15. Meurant, L. Corpus LSFB. First digital open access corpus of movies and annotations of French Belgian Sign Language (LSFB). Laboratoire de Langue des signes de Belgique francophone (LSFB-Lab). FRS-F.N.R.S et Université de Namur, 2015.  Morrissey, S., & Way, A. An example-based approach to translating sign language, In Workshop Example-Based Machine Translation (MT X–05), pages 109–116, Phuket, Thailand, September, 2005. Stokoe, William C. Sign Language Stucture: An Outline of the Visual Communication Systems of the American Deaf. Studies in Linguistics: Occasional papers 8, 1960. Tervoort, B. T. M. (1953). Structurele analyse van visueel taalgebruik binnen een groep dove kinderen. 

Modelling a parallel corpus of French and French Belgian Sign Language (LSFB)

RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHEAuthor(s) - Auteur(s) :Publication date - Date de publication :Permanent link - Permalien :Rights / License - Licence de droit d’auteur :Bibliothèque Universitaire Moretus PlantinInstitutional Repository - Research PortalDépôt Institutionnel - Portail de la Rechercheresearchportal.unamur.beModelling a parallel corpus of French and French Belgian Sign Language (LSFB)Meurant, Laurence; Gobert, Maxime; Cleve, AnthonyPublished in:Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016Publication date:2016Document VersionPeer reviewed versionLink to publicationCitation for pulished version (HARVARD):Meurant, L, Gobert, M & Cleve, A 2016, Modelling a parallel corpus of French and French Belgian SignL guage (LSFB). in Proceedings of the 10th International Conference on Language Resources and Evaluation,LREC 2016. European Language Resources Association (ELRA), pp. 4236-4240, LREC 2016 (10th LanguageResources and Evaluation Conference), Portoroz, Slovenia, 23/05/16. <http://www.lrec-conf.org/proceedings/lrec2016/index.html>General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 23. Jun. 2020Towards a corpus-based online tool for French - Sign Language (LSFB) needs The overarching objective underlying this proposal is to develop an online tool, based on a parallel corpus of French Belgian Sign Language and written French data, and aimed to assist a various set of tasks related to the comparison of LSFB and French. 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 comparative description of these languages. To the best of our knowledge, this kind of tool has never been created before.  The first step of investigation aims at creating a (unidirectional) French-LSFB (then, written-video) concordancer associated with a colocation calculator and to test the efficiency of this concordancer for the extraction of a dictionary of meanings in context.  In this proposal, we will present the modelling of the different data sources at our disposal and specifically the way they interact with one another.  Linguistic resources and Sign languages Sign languages (SLs) are among the less-resourced languages of the world, due to the combined impact of various factors including their status of minority languages, the lack of written form of these visual-gestural languages, and their only recent official acceptance and recognition in the society. Research in SL linguistics is generally considered to have begun with the works of Tervoort (1953) and Stokoe (1960). The foundation of the Sign Language Linguistics Society in 2004 symbolized that research on SLs had become a worldwide effort. The digital revolution has had an important impact on the knowledge of Sign Languages since it opened (in the early 2000s only)  the possibility to develop Corpus linguistics on sign languages by collecting, archiving, annotating and documenting large scale videotaped data (Johnston, 2010). Nowadays, annotating SL data remains a manual and time-consuming task. But this slow process is unavoidable at this stage in order to enlarge the available data needed to automate the process in a short future. We hypothesize that building our bilingual tool will contribute to this movement towards annotation automation.  In 2012 started in the University of Namur the “Corpus LSFB” project. The project will be published (online and open access) at the end of 2015. If this resource is essential to the linguistic description of LSFB, it is a potential wealth of information for pedagogic purposes, for the field of translation and interpretation studies and for the field of contrastive linguistics between signed and spoken languages.   From Sign language corpora to Sign-spoken languages tools Beyond the domain of SL linguistics, the computer revolution also impacted the domain of contrastive linguistics in general by having allowed the development of multilingual corpora. Multilingual corpora, combined with alignment and search tools, are today acknowledged for their theoretical as well as practical importance in cross-linguistic studies and applications: they provide a rich basis of languages correspondences in context that can serve as testbeds for linguistic theories and hypotheses, but they are also essential for applications in the fields of lexicography, natural language processing, automatic or machine-assisted translation and language teaching (Altenberg et al. 2002, Johansson, 2007). Multilingual corpora are the basis of all multilingual concordancer as TransSearch (Bourdaillet et al. 2010) or Linguee (Linguee 2015). Due to the visual-gestural nature of SLs, most modern SL machine-readable corpora, as the Corpus LSFB is, are multimodal corpora, since the videotaped data are accompanied by the written glosses of the signs and by the translation of the videos in written language. But as far as we know, this property of SL corpora has not been exploited yet for the development of bilingual tools. On the one hand, sign language engineering is mostly devoted to automatic or assisted translation tools (e.g. the ‘SignSpeak’ project1; Filhol et al., 2014; Dreuw et al., 2010; Morrissey et al., 2005), or for SL recognition (e.g. the ‘Dicta Sign’ project2; Dreuw et al., 2008). On the other hand, as SL corpora are recent and their number is still small, corpus-based SL dictionaries are scarce (the German Sign Language (DGS) dictionary in preparation3 is an exception).  The innovation of this research lies in making a SL corpus, i.e. the Corpus LSFB, an aligned and searchable translation corpus  (namely LSFB productions translated by human translators into French), providing aligned bilingual examples of words and signs in context, at the service of language teaching, translation, and contrastive linguistics. An overview of the possible user interface of the tool is shown in Figure 1 (the terms between asterisks refer to the entities’ names used in Figure 2).                                                  1 http://www.signspeak.eu/ 2 http://www.dictasign.eu/ 3 http://www.sign-lang.uni-hamburg.de/dgs-korpus/index.php/dictionary.html  Figure 1 : Model of the possible user interface based on fictive examples (inspired by the Linguee user interface). The terms between asterisks refer to the entities’ names used in Figure 2  Available resources  Data and tools at our disposal are: ●   LSFB data: 125 hours of HD, 50 f/sec. videos from “the Corpus LSFB”, containing semi-directed spontaneous productions of 50 pairs of signers from Brussels and all regions of Wallonia. The productions are elicited by a systematic list of 18 tasks guided by a deaf moderator and covering a variety of genres (narratives, conversations, explanations, argumentations and descriptions) and topics.  ●   LSFB annotations: 10 out of these 125 hours of videos are glossed, which means that each sign is given an ID-Gloss (Johnston, 2010), namely a written label of the lemma corresponding to the sign token (e.g. the ID-Gloss “PENSER” (‘think’) for all the possible forms of the verb). The annotation is made in ELAN4, which allows creating and searching annotations aligned with video sources. The annotation files are associated and time-aligned with the corresponding videos. ●   Translations: 5 out of the 10 hours of annotated data have a free translation in French. ●   Lex-LSFB: all the ID-Glosses entered in the annotation files (currently 2500 entries) are collected within an online, constantly evolving lexical database: Lex-LSFB. Each entry of Lex-LSFB includes the ID-Gloss of the sign, one or several possible translations of the sign in French, an animated GIF file showing the sign in isolation, and information about the variants of the sign. Lex-LSFB and ELAN are connected: each entry of Lex-LSFB is linked to the various occurrences of the sign in the videos. External tools and related data resources are also available: • CoBRA (Corpus Based Reading Assistant) is an online and interactive tool developed at University of Namur (Deville et al., 2013), based on bilingual corpora (Dutch-French and English-French) aligned at the level of the sentence, that allows the teachers to create labelled texts in Dutch (NL) or in English (EN) and French-speaking learners to be assisted in their reading by clicking on any word in order to know its meaning in its particular context of occurrence. The translation of each term is illustrated by a series of bilingual citations extracted from bilingual corpora. CoBRA is based on a searchable concordancer, called the “Dico Corpus” tool, and on two bilingual dictionaries (FR-NL and FR-EN) called “DiCoBRA” that are (1) produced from a contrastive approach of the existing dictionaries of each language and (2) completed by the contrastive data provided by “Dico Corpus”.   • CoBRA Corpus: the CoBRA resources currently include a global text corpus of over 30.000.000 words among which circa 15.000.000 French words, about 10.000.000 concordances (i.e.                                                 4 http://tla.mpi.nl/tools/tla-tools/elan/ aligned bilingual examples), an English-French glossary of about 19.000 entries, and a Dutch-French glossary of about 20.000 entries. • DiCoBRA: CoBRA’s dictionary includes circa 87.000 lemma and 300.000 inflected forms of French.  Modelling Corpus-LSFB data resources The first step of this project consisted in formally modelling the various data artifacts involved in the Corpus-LSFB and its environment. Figure 2 provides a simplified “helicopter-view” of this data model, by means of an Entity-Relationship (ER) diagram. This model represents the main concepts involved in the corpus, as well as their characteristics and relationships. Figure 2 : Simplified Entity-Relationship diagram of the data model The Corpus-LSFB consists of a set of videos (LSFB_VIDEO) where two signers achieved a task in LSFB. Each video is identified by a unique id, corresponding to its Unique Resource Identifier (uri), and is characterised by the duration of the video (Duration), and a brief description of the task (Task_Description).  The corpus also includes a large set of LSFB signs (LSFB), characterised by a unique id gloss (ID_Gloss). Each LFSB sign in the corpus is linked to a set of french keywords (FR_KEYWORD)  that represent the different possible meanings of the LSFB sign.  The occurrence of a given LSFB sign in a video is represented through entity type SIGN_ANNOTATION. An annotation indicates the exact time period during which the sign appears in the video, in the form of a time interval (Begin and End). Note that when the same sign S occurs N times in the very same video V, there are N annotations linking S and V in the corpus, each with a distinct time interval. The annotation also records which of the two signers is the author of the sign, via attribute Turn. By convention, the value of attribute Turn is either ‘A’ (signer A) or ‘B’ (signer B).  The corpus also provides, for a subset of the LSFB videos, the full French translation (FR_TRANSLATION) of the task. Each translation is made up of a set of French translation fragments (FR_TRANSLATION_FRAGMENT), that is a French text fragment (Text) translating what is expressed in LSFB by one of the two persons (Turn) during time interval [Begin, End] of the video. An external text corpus gracefully complements the Corpus-LSFB. This corpus consists of a large set of French texts, available through the CoBRA toolsuite. Those texts are in turn composed of French sentences (FR_SENTENCE), where contextual occurrences of each FR_KEYWORD (or one of its inflected forms) may possibly be found.    Populating the Corpus-LSFB data model Once the various Corpus-LSFB data artifacts have been modelled in a conceptual way, an important and challenging task remains to be done: populating the data mode in order to eventually build a queryable Corpus-LSFB database. At the time of writing this abstract, this task has almost been achieved in the sense that most data artifacts are currently recorded and/or referenced in the ELAN tool (LSFB_VIDEO, SIGN_ANNOTATION, FR_TRANSLATION and FR_TRANSLATION_FRAGMENT) and in the Lex-LSFB database (LSFB_SIGN and FR_KEYWORD). Both environments are connected to each other since the ELAN annotations make use of the same ID_Gloss values of the Lex-LSFB database. The external resources are mainly available through the CoBRA toolsuite, the database of which allows one to recover the different contexts of occurrence (FR_SENTENCE) of a French keyword (FR_KEYWORD), or of one of its inflected form, in a large aligned corpus of French texts (FR_TEXT).  Exploiting the Corpus-LSFB populated data model Once a complete and queryable Corpus-LSFB database will be available, we anticipate several usage scenarios of this database in the short term, as a first stage of the overall bilingual tool that we want to develop in the longer term.  First, we will need to develop a tool allowing us to further align (at the level of the French translation fragment) both sides of the Corpus LSFB, namely the annotated videos of LSFB productions and their French textual translations. The challenge of this task will be to automatically relate each sign annotation (i.e., an ID_Gloss and a [begin,end] time interval) to the corresponding part of the French translation fragment where the meaning of this sign is given in context. The [Begin, End] time interval of the French translation fragments will allow the alignment tool to identify the right fragment (the one that includes the [Begin, End] time interval of the sign). However, since the very same translation fragment may be linked to several (possibly numerous !) successive LSFB signs, the tool will then need to further slice the translation fragment in smaller fragments, each relating to one “clause-like” unit in LSFB. With the help of the alignment tool described above, we will derive a finer-grained Corpus-LSFB database, that we will then use as a basis to build advanced interactive tools and conduct systematic (contrastive) linguistic studies. The first interactive tool we want to build is a searchable concordancer that exploits the aligned, fine-grained Corpus-LSFB database as well as the external corpus in order to find the LSFB concordances related to a given input term in French, as shown in Figure 1.   From a scientific point of view, the very same database can also be exploited for conducting linguistic studies and enrich our knowledge of the contextual usages and meanings of a LSFB sign, but also to compare the ways (lexicon, paraphrase, depicting structures, etc.) French and LSFB express the same ideas.  References Altenberg, B. & Granger, S., eds. Lexis in contrast: corpus-based approaches. Vol. 7. John Benjamins Publishing, 2002. Bourdaillet, J., Huet, S., Langlais, P., & Lapalme, G. TransSearch: from a bilingual concordancer to a translation finder. Machine Translation, 24(3-4), 241-271, 2010. Deville, G., Dumortier, L., Meurisse, J-R. & Miceli, M. Ressources lexicales pour l’aide à l’apprentissage des langues In Gala, N. & Zock, M. (eds). Ressources Lexicales: Contenu, construction, utilisation, évaluation. John Benjamins, Vol 30 , p. 291-312, 2013. Dreuw, P., Ney, H., Pérez, G. M., Crasborn, O., Piater, J. H., Moya, J. M., & Wheatley, M. The SignSpeak Project-Bridging the Gap Between Signers and Speakers. In LREC, 2010. Dreuw, P., Neidle, C., Athitsos, V., Sclaroff, S., & Ney, H. Benchmark Databases for Video-Based Automatic Sign Language Recognition. In LREC, 2008. M. Filhol & Tannier, X. Construction of a French-LSF corpus, Building and Using Comparable Corpora. In LREC, 2014. Johnston, T. From archive to corpus: transcription and annotation in the creation of signed language corpora. International Journal of Corpus Linguistics, 15(1), 106-131, 2010. Johansson, Stig. Seeing through multilingual corpora. Language and Computers 62.1 (2007): 51-71. Linguee, http://www.linguee.com/, last visit 25/10/15. Meurant, L. Corpus LSFB. First digital open access corpus of movies and annotations of French Belgian Sign Language (LSFB). Laboratoire de Langue des signes de Belgique francophone (LSFB-Lab). FRS-F.N.R.S et Université de Namur, 2015.  Morrissey, S., & Way, A. An example-based approach to translating sign language, In Workshop Example-Based Machine Translation (MT X–05), pages 109–116, Phuket, Thailand, September, 2005. Stokoe, William C. Sign Language Stucture: An Outline of the Visual Communication Systems of the American Deaf. Studies in Linguistics: Occasional papers 8, 1960. Tervoort, B. T. M. (1953). Structurele analyse van visueel taalgebruik binnen een groep dove kinderen. 

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Modelling a parallel corpus of French and French Belgian Sign Language (LSFB)

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