Not logical: A distributional semantic account of negated adjectives Laura Aina Abstract: The meaning of a negated adjective does not always correspond to the one of its antonym (e.g., not small = large); indeed, linguistic theories and experimental data suggest that one of the functions of negation is to shift the meaning of the negated item but not necessarily flip it into the opposite (e.g. not small ≈ medium-sized). In this thesis, we study negated adjectives in English employing the perspective of Distributional Semantics. We first construct vectorial representations of these expressions based on their co-occurrences with contextual features in a large corpus. We then make use of these in a set of exploratory experiments aimed at clarifying their relationship with other expressions, such as antonyms (e.g. not small vs. large) and scale co-members (e.g., not small vs. tiny). In particular, we investigate negation in terms of pragmatic and “graded” notions which are apt to be studied in a distributional space: alternativehood, i.e., the degree of plausibility of alternatives to a negated item, and mitigation, i.e., the meaning shift from the original adjective. In addition, we design and evaluate a compositional method to model negation of adjectives as a function learnt directly from distributional data. Results suggest that negated adjectives have different profiles of use from other allegedly equivalent classes of expressions, and that, contrarily to what often is assumed, a data-driven modelling of negation is not entirely out of the scope of distributional methods. Overall, this thesis tackles research questions about the complex nature of negation and the open problem of modelling this phenomenon within Distributional Semantics.