By Fidelia Ibekwe-SanJuan, Anne Condamines, M. Teresa Cabre Castellvi
A standard framework below which many of the experiences on terminology processing may be considered is to think about not just the texts from which the terminological assets are equipped yet rather the functions detailed. the present ebook, first released as a different factor of Terminology 11:1 (2005), analyses the impact of purposes on time period definition and processing. varieties of purposes were pointed out: middleman and terminal functions (involving finish users). middleman purposes drawback the construction of terminological wisdom assets reminiscent of domain-specific dictionaries, ontologies, glossary or taxonomies. those wisdom assets then shape the inputs to terminal purposes akin to info extraction, details retrieval, technology and expertise watch or computerized booklet index development. many of the purposes handled within the booklet fall into the 1st classification. This ebook represents the 1st test, from a pluridisciplinary perspective, take into consideration the function of purposes within the processing of terminology.
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Additional resources for Application-driven terminology engineering
A non-formal definition aims “to define in a general sense so that a reader can see the familiar element in whatever the new term may be” (Trimble 1985). It can be an association with a synonym, a paraphrase or grammatical derivation; – those based on the type of relation binding the definiendum and the definiens. Martin (1990: pp. 86–95) considers 6 categories of definitions: synonymic, antonymic, derivational, approximate, metonymic, hypernymic. In this latter category, and working on French, Auger (1997) compiles the work of Chuckwu and Thoiron (1989: 23–53), Condamines (1993: 25–65), Loffler-Laurian (1983: 8–20), and Martin (1983, 1992), and proposes a unified typology, in which we can find all the previous categories presented in this section.
Baby). Table 9. 2 Evaluation of the horizontal structuring We have tagged defining contexts in the test corpus, and built a similarity table between terms on the basis of the number of words that they shared in extended defining contexts. , which shared at least one definition word (first column), the numeric range of words shared between definienda (second column), the number of terms correctly related as potential ontological siblings (third column), the number of terms related as well as the semantic relation different from co-hyponymy (fourth column) and the number of erroneously related terms (fifth column).
Once the defining contexts are extracted and validated, we additionally rely on their lexical content to propose ontology siblings, which corresponds to the horizontal modelling. We test the possibility to link ontology siblings on the basis of the words that they share in their definitions, and we hypothesise that these common words can be a clue to characterise their common semantic feature. For instance, given the following dictionary definitions of mother and father: 29 30 Véronique Malaisé, Pierre Zweigenbaum and Bruno Bachimont Mère: femme qui a donné naissance à un ou plusieurs enfants (Engl.
Application-driven terminology engineering by Fidelia Ibekwe-SanJuan, Anne Condamines, M. Teresa Cabre Castellvi
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