  In this work, we propose a Distributional Semantic resource enriched with
linguistic and lexical information extracted from electronic dictionaries,
designed to address the challenge of bridging the gap between the continuous
semantic values represented by distributional vectors and the discrete
descriptions offered by general semantics theory. Recently, many researchers
have concentrated on the nexus between embeddings and a comprehensive theory of
semantics and meaning. This often involves decoding the representation of word
meanings in Distributional Models into a set of discrete, manually constructed
properties such as semantic primitives or features, using neural decoding
techniques. Our approach introduces an alternative strategy grounded in
linguistic data. We have developed a collection of domain-specific
co-occurrence matrices, derived from two sources: a classification of Italian
nouns categorized into 4 semantic traits and 20 concrete noun sub-categories,
and a list of Italian verbs classified according to their semantic classes. In
these matrices, the co-occurrence values for each word are calculated
exclusively with a defined set of words pertinent to a particular lexical
domain. The resource comprises 21 domain-specific matrices, one comprehensive
matrix, and a Graphical User Interface. Our model facilitates the generation of
reasoned semantic descriptions of concepts by selecting matrices directly
associated with concrete conceptual knowledge, such as a matrix based on
location nouns and the concept of animal habitats. We assessed the utility of
the resource through two experiments, achieving promising outcomes in both: the
automatic classification of animal nouns and the extraction of animal features.
