This article presents a study to distinguish and quantify the various types of semantic associations provided by humans, to investigate their properties, and to discuss the impact that our analyses may have on NLP tasks. Specifically, we concentrate on two issues related to word properties and word relations: (1) We address the task of modelling word meaning by empirical features in data-intensive lexical semantics. Relying on large-scale corpus-based resources, we identify the contextual categories and functions that are activated by the associates and therefore contribute to the salient meaning components of individual words and across words. As a result, we discuss conceptual roles and present evidence for the usefulness of co-occurrence information in distributional descriptions. (2) We assume that semantic associates provide a means to investigate the range of semantic relations between words and contexts, and we provide insight into which types of semantic relations are treated as important or salient by the speakers of the language.