Dans cette thèse, nous envisageons la question de l’ambiguïté des requêtes soumises à un moteur de recherche dans un domaine particulier qui est l’actualité. Nous nous appuyons sur les travaux récents dans le domaine de la recherche d’information (RI) qui ont montré l’apport d’informations contextuelles pour mieux cerner et traiter plus adéquatement le besoin informationnel. Nous faisons ainsi l’hypothèse que les éléments d’information disponibles dans une application de RI (contextes présents dans la base documentaire, répétitions et reformulations de requêtes, dimension diachronique de la recherche) peuvent nous aider à étudier ce problème d’ambiguïté. Nous faisons également l’hypothèse que l’ambiguïté va se manifester dans les résultats ramenés par un moteur de recherche. Dans ce but, nous avons mis en place un dispositif pour étudier l’ambiguïté des requêtes reposant sur une méthode de catégorisation thématique des requêtes, qui s’appuie sur unecatégorisation experte. Nous avons ensuite montré que cette ambiguïté est différente de celle repérée par une ressource encyclopédique telle que Wikipédia. Nous avons évalué ce dispositif de catégorisation en mettant en place deux tests utilisateurs. Enfin, nous fournissons une étude basée sur un faisceau d’indices contextuels afin de saisir le comportement global d’une requête. / In this thesis, we consider the question of the ambiguity of queries submitted to a search engine in a particular area that is news.We build on recent work in the field of information retrieval (IR) that showed the addition of contextual information to better identify and address more adequately the information need. On this basis, we make the hypothesis that the elements of information available in an application of IR (contexts in the collection of documents, repetitions and reformulations of queries, diachronic dimension of the search) can help us to examine this problem of ambiguity. We also postulate that ambiguity will manifest in the results returned by a search engine. In this purpose to evaluate these hypotheses, we set up a device to study the ambiguity of queries based on a method of thematic categorization of queries, which relies on an expert categorization. We then show that this ambiguity is different which is indicated by an encyclopedic resources such as Wikipedia.We evaluate this categorization device by setting up two user tests. Finally, we carry out a study based on a set of contextual clues in order to understand the global behavior of a query.

<p dir="ltr">This research investigates the use of Large Language Model (LLM) embeddings to capture the unique stylistic features of authors in Authorship Attribution (AA) tasks. Specifically, the focus of this research is on evaluating whether LLM-generated embeddings can effectively capture stylistic nuances that distinguish different authors, ultimately assessing their utility in tasks such as authorship attribution and clustering.The dataset comprises news articles from The Guardian authored by multiple writers, and embeddings were generated using OpenAI's text-embedding-ada-002 model. These embeddings were subsequently passed through a Siamese network with the objective of determining whether pairs of texts were authored by the same individual. The resulting model was used to generate style embeddings for unseen articles, which were then evaluated through classification and cluster analysis to assess their effectiveness in identifying individual authors across varying text samples. The classification task tested the model's accuracy in distinguishing authors, while the clustering analysis examined whether style embeddings primarily captured authorial identity or reflected domain-specific topics.</p><p dir="ltr">Our findings demonstrate that the proposed architecture achieves high accuracy for authors not previously encountered, outperforming traditional stylometric features and highlighting the effectiveness of LLM-based style embeddings. Additionally, our experiments reveal that authorship attribution accuracy decreases as the number of authors increases, yet improves with longer text lengths. </p><p dir="ltr"><br></p>