Titel: Translating Action Verbs using a Dictionary of Images: the IMAGACT Ontology
Personen:Panunzi, Alessandro/De Felice, Irene/Gregori, Lorenzo/Jacoviello, Stefano/Monachini, Monica/Moneglia, Massimo/Quochi, Valeria/Russo, Irene
Jahr: 2014
Typ: Aufsatz
Verlag: Institute for Specialised Communication and Multilingualism
Ortsangabe: Bolzano/Bozen
In: Abel, Andrea/Vettori, Chiara/Ralli, Natascia: Proceedings of the 16th EURALEX International Congress: The User in Focus, Bolzano/Bozen, Italien 15 - 19 July 2014
Seiten: 1163-1170
Untersuchte Sprachen: Chinesisch*Chinese - Englisch*English - Italienisch*Italian - Spanisch*Spanish
Schlagwörter: audio-visuelle Medien/Multimedia*audio-visual media/multimedia
Illustration*illustration/figure
korpusbasierte Lexikografie*corpus-based lexicography
Übersetzung*translation
zweisprachige bzw. mehrsprachige Lexikografie*bilingual/multilingual lexicography
Medium: Online
URI: http://euralex.org/category/publications/euralex-2014/
Zuletzt besucht: 22.10.2018
Abstract: Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble in all translation tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the IMAGACT search interface and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants, without intense lexicographic work involving underdetermined semantic descriptions.