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<dc:title xml:lang="fr">Une méthode automatique de construction de corpus de reformulation</dc:title>
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<dc:subject xml:lang="fr">Corpus de reformulation médicale</dc:subject>
<dc:subject xml:lang="fr">Annotation semi-automatique</dc:subject>
<dc:subject xml:lang="fr">Guide d'annotation de reformulations</dc:subject>
<dc:subject xml:lang="fr">Marqueurs de reformulation</dc:subject>
<dc:subject xml:lang="fr">Analyse lexicale et sémantico-pragmatique</dc:subject>
<dc:subject xml:lang="fr">Lisibilité des reformulations</dc:subject>
<dc:subject xml:lang="fr">Génération et classification automatique de reformulations</dc:subject>
<dc:subject xml:lang="en">Medical paraphrase corpus</dc:subject>
<dc:subject xml:lang="en">Semi-automatic annotation</dc:subject>
<dc:subject xml:lang="en">Paraphrase annotation guide</dc:subject>
<dc:subject xml:lang="en">Paraphrase markers</dc:subject>
<dc:subject xml:lang="en">Lexical and semantico-pragmatic analysis</dc:subject>
<dc:subject xml:lang="en">Readability of paraphrases</dc:subject>
<dc:subject xml:lang="en">Automatic generation and classification of paraphrases</dc:subject>
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<dcterms:abstract xml:lang="fr">Notre thèse a comme objectif la mise en place d’une méthode semi-automatique de construction des corpus de reformulations sous-phrastiques médicales, en français et en roumain. Nous définissons la reformulation sous-phrastique comme l’équivalence basée sur un noyau sémantique commun, située dans l’empan d’une phrase, qui contribue à la vulgarisation médicale. Notre méthode consiste, d’une part, dans l’exploitation des corpus comparables et des marqueurs pour identifier automatiquement des termes médicaux et leurs reformulations et, d’autre part, dans l’utilisation des architectures à base de réseaux de neurones pour la reconnaissance et la génération automatique de la reformulation. Nous avons construit le premier corpus de textes de vulgarisation médicale en roumain de grande taille, GrandMed-Ro2. Nous avons annoté manuellement et réalisé une analyse linguistique de 19 890 phrases (57% ont une double annotation). Les 11 653 paires de termes médicaux - reformulations validées constituent le corpus RefoMed. Nous évaluons la lisibilité des reformulations pour le grand public et nous analysons 11 314 prédictions de reformulations générées automatiquement.</dcterms:abstract>
<dcterms:abstract xml:lang="en">The objective of our thesis is to set up a semi-automatic method for the construction of medical subphrastic paraphrase corpora in French and Romanian. We define the sub-phrastic reformulation as the equivalence based on a common semantic core, within a sentence, which contributes to the popularization of medical terms for lay people. Our method consists, on the one hand, in the exploitation of comparable corpora and markers to automatically identify medical terms and their paraphrases and, on the other hand, in the use of neural network architectures for the automatic recognition and generation of the paraphrase. We built the first large corpus of Romanian medical popularization texts, GrandMed-Ro2. We manually annotated and performed a linguistic analysis of 19,890 sentences (57% have a double annotation). The 11,653 validated medical term-paraphrase pairs constitute the RefoMed corpus. We evaluate the readability of the paraphrases for the general public and analyse 11,314 automatically generated paraphrase predictions.</dcterms:abstract>
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