Exploiting Systematicity: a Connectionist Model of Bootstrapping in Language Acquisition Hélène Tourigny Abstract: This thesis presents a connectionist model of syntactic bootstrapping processes in language acquisition. According to the Syntactic Bootstrapping hypothesis, children acquiring language can learn (part of) the meaning of new words based on the syntactic context in which they appear. Psycholinguistic research has shown that children can indeed use morphosyntactic cues to guide their interpretation of novel words. This project investigates whether a connectionist network can exploit systematicity in language to acquire novel words over the course of development. The network is trained according to a semi-supervised algorithm. The model learns a sentence-interpretation task from both labelled and unlabelled data. Specifically, it learns to output a semantic representation (roughly corresponding to ‘who did what to whom’) for given sentences. To investigate whether syntactic bootstrapping can successfully lead to lexical development, some vocabulary items are only presented in unlabelled sentences. To correctly process these examples and learn these words, the network must infer the novel words’ properties (e.g. grammatical category, animacy features) based on the context in which they appear. The network must then use its own output (i.e. its interpretation of the sentences) to train itself. The system’s ability to rely on syntactic cues for vocabulary acquisition is tested in a number of experiments. Although the network is able to acquire the language and shows very good generalization, its ability to rely on syntactic bootstrapping to learn novel words does not meet expectations.