Distributional semantics models (DSMs) are known to produce excellent representations of word meaning, which correlate with a range of behavioural data. As lexical representations, they have been said to be fundamentally different from truth-theoretic models of semantics, where meaning is defined as a correspondence relation to the world. There are two main aspects to this difference : a) DSMs are built over corpus data which may or may not reflect ‘what is in the world’ ; b) they are built from word co-occurrences, that is, from lexical types rather than entities and sets. In this paper, we inspect the properties of a distributional model built over a set-theoretic approximation of ‘the real world’. To achieve this, we take the annotation a large database of images marked with objects, attributes and relations, convert the data into a representation akin to first-order logic and build several distributional models using various combinations of features. We evaluate those models over both relatedness and similarity datasets, demonstrating their effectiveness in standard evaluations. This allows us to conclude that, despite prior claims, truth-theoretic models are good candidates for building graded lexical representations of meaning.

We present the design of a system for making sense of conflicting rules expressed in a fragment of the prominent controlled natural language ACE, yet extended with means of expressing defeasible rules in the form of normality assumptions. The approach we describe is ultimately based on answer-set-programming (ASP) ; simulating existential quantification by using skolemization in a manner resembling a translation for ASP recently formalized in the context of -ASP. We discuss the advantages of this approach to building on the existing ACE interface to rule-systems, ACERules.

In recent years, both cognitive and computational research has provided empirical analyses of contextual co-occurrence of concrete and abstract words, partially resulting in inconsistent pictures. In this work we provide a more fine-grained description of the distributional nature in the corpus-based interaction of verbs and nouns within subcategorisation, by investigating the concreteness of verbs and nouns that are in a specific syntactic relationship with each other, i.e., subject, direct object, and prepositional object. Overall, our experiments show consistent patterns in the distributional representation of subcategorising and subcategorised concrete and abstract words. At the same time, the studies reveal empirical evidence why contextual abstractness represents a valuable indicator for automatic non-literal language identification.