abstract: Many neurological disorders, especially those that result in dementia, impact speech and language production. A number of studies have shown that there exist subtle changes in linguistic complexity in these individuals that precede disease onset. However, these studies are conducted on controlled speech samples from a specific task. This thesis explores the possibility of using natural language processing in order to detect declining linguistic complexity from more natural discourse. We use existing data from public figures suspected (or at risk) of suffering from cognitive-linguistic decline, downloaded from the Internet, to detect changes in linguistic complexity. In particular, we focus on two case studies. The first case study analyzes President Ronald Reagan’s transcribed spontaneous speech samples during his presidency. President Reagan was diagnosed with Alzheimer’s disease in 1994, however my results showed declining linguistic complexity during the span of the 8 years he was in office. President George Herbert Walker Bush, who has no known diagnosis of Alzheimer’s disease, shows no decline in the same measures. In the second case study, we analyze transcribed spontaneous speech samples from the news conferences of 10 current NFL players and 18 non-player personnel since 2007. The non-player personnel have never played professional football. Longitudinal analysis of linguistic complexity showed contrasting patterns in the two groups. The majority (6 of 10) of current players showed decline in at least one measure of linguistic complexity over time. In contrast, the majority (11 out of 18) of non-player personnel showed an increase in at least one linguistic complexity measure. / Dissertation/Thesis / Masters Thesis Computer Science 2016

Le présent travail aborde le thème de la complexité sémantique dans le langage naturel, et il propose une hypothèse basée sur certaines caractéristiques des phrases du langage naturel qui déterminent la difficulté pour l'interpretation humaine.Nous visons à introduire un cadre théorique général de la complexité sémantique de la phrase, dans lequel la difficulté d'élaboration est liée à l'interaction entre deux composants: la Mémoire, qui est responsable du rangement des représentations d'événements extraites par des corpus, et l'Unification, qui est responsable de la combinaison de ces unités dans des structures plus complexes. Nous proposons que la complexité sémantique depend de la difficulté de construire une représentation sémantique de l'événement ou de la situation exprimée par une phrase, qui peut être récupérée directement de la mémoire sémantique ou construit dynamiquement en satisfaisant les contraintes contenus dans les constructions.Pour tester nos intuitions, nous avons construit un Distributional Semantic Model pour calculer le coût de composition de l'unification des phrases. Les tests sur des bases de données psycholinguistiques ont révélé que le modèle est capable d'expliquer des phénomènes sémantiques comme la mise à jour context-sensitive des attentes sur les arguments et les métonymies logiques. / The present work deals with the problem of the semantic complexity in natural language, proposing an hypothesis based on some features of natural language sentences that determine their difficulty for human understanding. We aim at introducing a general framework for semantic complexity, in which the processing difficulty depends on the interaction between two components: a Memory component, which is responsible for the storage of corpus-extracted event representations, and a Unification component, which is responsible for combining the units stored in Memory into more complex structures. We propose that semantic complexity depends on the difficulty of building a semantic representation of the event or the situation conveyed by a sentence, that can be either retrieved directly from the semantic memory or built dynamically by solving the constraints included in the stored representations.In order to test our intuitions, we built a Distributional Semantic Model to compute a compositional cost for the sentence unification process. Our tests on several psycholinguistic datasets showed that our model is able to account for semantic phenomena such as the context-sensitive update of argument expectations and of logical metonymies.