Claire_Gardent

Claire Gardent

Claire Gardent

French computer scientist

Claire Gardent is a French computer scientist and linguist specializing in natural language processing, including natural language generation and machine translation. She is a director of research at the French National Centre for Scientific Research, affiliated with the Lorraine Research Laboratory in Computer Science [fr] (LORIA),[1] She is also past chair of the European chapter of the Association for Computational Linguistics, and former editor-in-chief of the journal Traitement Automatique des Langues (Revue TAL).[2]

Education and career

Gardent was a linguistics student at the University of Toulouse, graduating in 1986. She went to the UK for graduate study, earning a master's degree in artificial intelligence from the University of Essex in 1987 and a PhD in cognitive science from the University of Edinburgh in 1991.[3] Her doctoral dissertation, Gapping and VP ellipsis in a unification-based grammar, was jointly supervised by Ewan Klein and Robin Cooper.[4]

After ten years as a postdoctoral researcher in the Netherlands and Germany, she joined CNRS and LORIA as a researcher in 2000.[3] She has headed a research group on computational, formal, and field linguistics since 2019.[1]

Books

Gardent is the coauthor of books including:

  • Techniques d'analyse et de génération pour la langue naturelle (with Karine Baschung, Editions Adosa, 1995)[5]
  • Deep Learning Approaches to Text Production (with Shashi Narayan, Morgan & Claypool, 2020).[6]

Recognition

In 2022 she won the CNRS Silver Medal.[1]

References

  1. [1]
    Claire Gardent : une médaille d’argent du CNRS pour le traitement automatique du langage, CNRS, retrieved 2023-02-22
  2. [2]
    Claire Gardent, LORIA, 22 March 2013, retrieved 2023-02-22
  3. [3]
    "Annexe: curriculum vitae des membres du projet", L'action PASSAGE, INRIA, 2006, retrieved 2023-02-22
  4. [4]
    Claire Gardent at the Mathematics Genealogy Project
  5. [5]
    Reviews of Techniques d'analyse et de génération pour la langue naturelle:
  6. [6]
    Review of Deep Learning Approaches to Text Production:
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