Considérons la métaphore comme une analogie à une inconnue. L’expliquer revient à résoudre l’unique variable du carré analogique qui en résulte et dont les trois autres termes sont fixés. Nous proposons ici une méthode détaillée pour arriver à cet objectif en utilisant la base de connaissances JeuxDeMots . Nous procédons par reconnaissance de schémas de relations préalablement identifiés et qui permettent d’évaluer la force de la similarité relationnelle et celles des deux similarités attributionnelles pour en déduire celle de l’analogie dans sa globalité. Le terme candidat qui permet d’obtenir la meilleure force d’analogie entre les quatre termes de l’analogie à trou ainsi complétée est élu. Enfin, on cherche à démontrer que l’utilisation d’inférences dans ce processus permet d’aboutir à de meilleurs résultats, c’est-à-dire augmenter le nombre de fois où un bon candidat est élu.

We are interested in the semantic relations conveyed by polylexical entities in the postnominal prepositional noun phrases form “A de B” (A of B). After identifying a relevant set of semantic relations types, we proceed, using generative AI, to build a collection of phrases, for each semantic relation type identified. We propose an algorithm for creating rules that allow the selection of the relation between A and B in noun phrases of each type. These rules correspond to selecting from a knowledge base the appropriate neighborhood of a given term. For the phrase “désert d’Algérie” carrying the location relation, the term “désert” is identified as a geographical location, and “Algérie” as a country. These constraints are used to automatically learn a set of rules for selecting the location relation for this type of example. Rules are not exclusive as there may be instances that fall under multiple relations. In the phrase “portrait de sa mère - the portrait of his/her mother”, all of depiction, possession, and producer types are a possible match.

In 2019, about 293 billion emails were sent worldwide every day. They are a valuable source of information and knowledge for professionals. Since the 90’s, many studies have been done on emails and have highlighted the need for resources regarding numerous NLP tasks. Due to the lack of available resources for French, very few studies on emails have been conducted. Anaphora resolution in emails is an unexplored area, annotated resources are needed, at least to answer a first question: Does email communication have specifics that must be addressed to tackle the anaphora resolution task? In order to answer this question 1) we build a French emails corpus composed of 100 anonymized professional threads and make it available freely for scientific exploitation. 2) we provide annotations of anaphoric links in the email collection.