Modelling Syntactic and Semantic Tasks with Linguistically Enriched Recursive Neural Networks Jonathan Mallinson Abstract: In this thesis a compositional distributional semantic approach, the Recursive Neural Network, is used to syntactically-semantically compose non-symbolic representations of words. Unlike previous Recursive Neural Network models which use either no linguistic enrichment or significant symbolic syntactic enrichment, I propose minimal linguistic enrichments which are both semantic and syntactic. I achieve this by enriching the Recursive Neural Networks' models with core syntactic/semantic linguistic types: head, argument and adjunct. This approach brings together formal linguistics and computational linguistics, as such I give a broad account of these theories. The syntactic understanding of the model is tested by a parsing task and the semantic understanding is tested by a paraphrase detection task. The results of these tasks not only show the benefits of linguistic enrichment but also raise further questions of study.