The European Parliament Interpreting Corpus (EPIC): implementation and developments
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
The call for the creation of corpora in Interpreting Studies that could be queried by means of Corpus Linguistics tools was first made by Shlesinger (1998) over a decade ago. However, only recently has this need started to be met. The European Parliament Interpreting Corpus (EPIC) is one of the first machine-readable corpora to be openly accessible in the field of Interpreting Studies. It was created in 2004/2006 by the Directionality Research Group of the University of Bologna at Forlì, and consists of 9 sub-corpora in total: three sub-corpora of source language speeches (Italian, English and Spanish) and six sub-corpora of simultaneously interpreted speeches, thus comprising all possible directions and combinations of the three languages involved (Monti et al. 2005, Sandrelli et al.. 2010). At present, the corpus includes only a small part of all the recorded material, which is stored in the EPIC Multimedia Archive. \nThe present paper describes the steps undertaken to create the corpus and the ongoing developments to further expand it and improve its structure. Firstly, the methodology used for user-friendly data collection and transcription and for the part-of-speech (POS) tagging and lemmatisation of this open corpus will be described; then, the web-interface developed to carry out simple and advanced queries on-line will be illustrated (see http://sslmitdev-online.sslmit.unibo.it/corpora/corporaproject.php?path=E.P.I.C.). Examples of the corpus-based studies carried out so far will be provided (Russo et al 2006, Bendazzoli et al 2011) and a special emphasis will be placed on the great potential of EPIC as a pedagogical and research tool in interpreter training. Interpreting students can transcribe and analyse part of the recorded material stored in the EPIC Multimedia Archive in their graduation dissertations, thus taking advantage of a unique opportunity to reflect upon real-life professional interpreting performances and upon their own learning process. Finally, ongoing developments and future steps will be discussed: text-to-sound and source text-to-target text alignment procedures are currently being tested, so as to make EPIC a more powerful resource to be explored by the interpreting research community \n \n \nReferences \nBENDAZZOLI, C., SANDRELLI, A. AND M. RUSSO (2011) “Disfluencies in simultaneous interpreting: a corpus-based analysis”, in A. Kruger, K. Walmach and J. Munday (eds.) Corpus-based Translation Studies: Research and Applications, London /New York: Continuum, 282-306. \nMONTI, C., BENDAZZOLI, C., SANDRELLI A. AND M. RUSSO (2005) “Studying Directionality in Simultaneous Interpreting through an Electronic Corpus: EPIC (European Parliament Interpreting Corpus)” paper presented at the International Symposium “Pour une traductologie proactive” organised for the 50° anniversary of META, University of Montreal, 6th-9th April 2005, (vol 50:4). Online: http://www.erudit.org/revue/meta/2005/v50/n4/019850ar.pdf \nRUSSO, M., BENDAZZOLI, C. E A. SANDRELLI (2006) "Looking for Lexical Patterns in a Trilingual Corpus of Source and Interpreted Speeches: Extended Analysis of EPIC (European Parliament Interpreting Corpus)", Forum, vol. 4:1, 221-254. \nSANDRELLI, A., BENDAZZOLI, C. AND M. RUSSO (2010) “European Parliament Interpreting Corpus (EPIC): Methodological issues and preliminary results on lexical patterns in SI”, International Journal of Translation 22 (1-2), 165-203. \nSHLESINGER, M. (1998): “Corpus-based interpreting studies as an offshoot of corpus-based translation studies”, META, 43-4, pp. 486-493.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle