This Ph.D. defense will take place on Wednesday, November 8th, 2023, at 10 AM, in room EULER VIOLET – INRIA, 2004 route des Lucioles – BP 93, 06902 Sophia-Antipolis. The defense can also be followed online: https://inria.webex.com/inria/j.php?MTID=m0f7ea6b7a7aa191b786ca5808aed31d7
The presentation will be in English.
Thesis committee:
Véronique MORICEAU, Maître de conférences HDR – Paul Sabatier Toulouse University 3 (Reviewer)
Frédérique SEGOND, Professeur des Universités, HDR, Inria (Reviewer)
Maurizio FILIPPONE, Professeur des Universités, EURECOM (Jury President)
Serena VILLATA, Directeur de Recherche, HDR, Université Côte d’Azur (Ph.D. advisor)Elena CABRIO, Professeur des Universités, HDR, Université Côte d’Azur (Ph.D. advisor)
Abstract:
The automated analysis of argumentation has garnered significant interest in recent years, as computational methods stand to enhance discourse quality across domains. This is especially pertinent in complex fields like healthcare, where sound reasoning bears direct impacts on human lives. The work presented in this thesis advances the state-of-the-art in argument mining and quality assessment, crafted to the intricacies of the medical domain. The thesis makes four main contributions: (1) Development and application of argument mining techniques, including analysis of their use in various domains and contributions to COVID-19 research. (2) Argumentation quality assessment methods, including annotation of a new dataset of 402 student essays with quality dimensions like cogency, rhetoric, and reasonableness. Innovative neural architectures combining textual features and graph embeddings are shown to aptly classify these facets, obtaining .78 F1, .89 F1, and 0.54 F1 respectively. (3) Identification of potential premises in the medical domain by automatically analyzing symptoms from 314 clinical cases and aligning them with external knowledge sources such as the Human Phenotype Ontology (HPO) using contextual embeddings (.53 accuracy). (4) Development of a transparent prevalence function to rank the explanatory power of the identified premises, leveraging statistics like abnormality and uniqueness from the knowledge base.This thesis makes significant contributions to the fields of argument mining and quality assessment through the development of novel techniques and resources. The proposed methods push the boundaries of automatic argument analysis, while the specially crafted datasets provide new opportunities for data-driven research. A major highlight is the tailored application to the medical domain, which required adapting argumentation notions and objectives to suit this complex field. The thesis enhances our theoretical understanding of quality modelling and delivers practical advancements in argument mining. By connecting insights across domains, it paves the way for future interdisciplinary research at the intersection of argumentation, machine learning, and specialized disciplines like healthcare.
