Exploring terminological relations between multi-word terms in distributional semantic models

Bibliographic Data

ID20395193
AuthorsYizhe Wang (0000-0002-0711-8218, Université Toulouse - Jean Jaurès), Béatrice Daille (0000-0001-9095-7372, Nantes Université), Nabil Hathout (0000-0003-4492-171X, CNRS, Laboratoire Cognition, Langues, Langage, Ergonomie (CLLE))
Year2024
Volume30
Issue2
Pages159-189
Publication date2024-10-10
Open AccessYes
TypeARTICLE
VenueTerminology International Journal of Theoretical and Applied Issues in Specialized Communication (JOURNAL)
Journal identifiersISSN: 0929-9971 • E-ISSN: 1569-9994
PublisherJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/term.21053.wan
OpenAlexW4382340716
LanguageEN
Citations received1
References cited41

A term is a lexical unit with specialized meaning in a particular domain. Terms may be simple (STs) or multi-word (MWTs). The organization of terms gives a representation of the structure of domain knowledge, which is based on the relationships between the concepts of the domain. However, relations between MWTs are often underrepresented in terminology resources. This work aims to explore distributional semantic models for capturing terminological relations between multi-word terms through lexical substitution and analogy. The experiments show that the results of the analogy-based method are globally better than those of the one based on lexical substitution and that analogy is well suited to the acquisition of synonymy, antonymy, and hyponymy while lexical substitution performs best for hypernymy

Analogy · Lexical item · Linguistics · Natural language processing · Terminology · Computer Science · linguistics and terminology studies · Mathematics · Natural Language Processing Techniques · Philosophy · Topic Modeling · Artificial Intelligence

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