2014
DOI: 10.33011/lilt.v11i.1369
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Démonette, a French derivational morpho-semantic network

Abstract: Démonette is a derivational morphological network created from information provided by two existing lexical resources, DériF and Morphonette. It features a formal architecture in which words are associated with semantic types and where morphological relations, labelled with concrete and abstract bi-oriented definitions, connect derived words with their base and indirectly related words with each other.

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Cited by 13 publications
(12 citation statements)
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“…They will be gradually supplemented with new data based on further filtering using morphological analyses. Further analyses could also make it possible to use this data to enrich existing resources of derivational networks, such as Démonette (Hathout & Namer 2014), adding both new word forms and diachronic frequency information. However, it should be borne in mind that such a large amount of data will necessarily contain a certain percentage of errors, which will only become apparent in further analyses.…”
Section: Discussionmentioning
confidence: 99%
“…They will be gradually supplemented with new data based on further filtering using morphological analyses. Further analyses could also make it possible to use this data to enrich existing resources of derivational networks, such as Démonette (Hathout & Namer 2014), adding both new word forms and diachronic frequency information. However, it should be borne in mind that such a large amount of data will necessarily contain a certain percentage of errors, which will only become apparent in further analyses.…”
Section: Discussionmentioning
confidence: 99%
“…In contrast, there are few resources for derivational morphology in French. One of the first is Démonette-1 (Hathout & Namer, 2014a, 2014c. More recently, a French derivational lexicon has been integrated into the UniMorph 5 database (Batsuren et al, 2022).…”
Section: Related Workmentioning
confidence: 99%
“…(37) a. verb; action_N, agt_N, ins_N b. verb; action_N, agt_N, Ø c. verb; place_N, action_N, agt_N, ins_N d. verb; action_N, agt_N, ins_N, rslt_N e. verb; action_N, agt_N, ins_N, pat_N etc. In a complementary way, it seems crucial to have an idea of the number of derivational families that fit each network (for French, using Démonette (Hathout and Namer, 2014)) and of the frequency of their occurrences in corpora. Derivational paradigms can help to make inferences only if these figures are known.…”
Section: The Organization Of the Action Networkmentioning
confidence: 99%