Exploring the Lexis of Art Through a Specialized Corpus: A Bilingual Italian-English Perspective

  •  Antonella Luporini    


This study presents an application of a specialized corpus, including texts specifically related to art and cultural heritage, to the analysis of artistic vocabulary in a bilingual (Italian-English) perspective, focusing on the Italian lemmas opera, figura and disegno and their English translation equivalents. The starting point is the Italian corpus that is being developed under the research project Lessico multilingue dei beni culturali (‘Multilingual art and cultural heritage vocabulary’, LBC), available online in open access through NoSketchEngine. First, a lemmatized nounlist ordered by frequency of occurrence is extracted from the corpus, leading to the selection of the above-mentioned focus words, in view of both their frequency and status as technical terms within the domain of art (though exhibiting different levels of technicality). These are further investigated by extracting collocates and KWIC concordances, leading to the identification of several specialized collocations and domain-specific senses. The analysis subsequently moves from corpus to dictionary, exploring the extent to which the patterns emerging from corpus investigation are accounted for in the entries for opera, figura and disegno in four Italian-English bilingual dictionaries. From this viewpoint, the study also aims to show how specialized corpus data can be used for the extraction of collocations, terms, and context-specific word senses, which may in turn be used both to enrich the information provided by currently available general dictionaries, and to work towards the creation of a large-scale specialized bilingual dictionary, which is non-existent to date.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1923-869X
  • ISSN(Online): 1923-8703
  • Started: 2011
  • Frequency: bimonthly

Journal Metrics

Google-based Impact Factor (2021): 1.43

h-index (July 2022): 45

i10-index (July 2022): 283

h5-index (2017-2021): 25

h5-median (2017-2021): 37

Learn more