Object Classification as Key for Algorithmic Language Processing of Metonymy and Metaphor : A Sensory Language Retrieval Approach+
Daniel Nergui-Szollossy · 2019
This paper proposes a Sensory Language Retrieval (SLR) algorithm and implicitly initiates a new approach. The algorithm utilizes the relationship between meaning and sensory functions by assigning human sensory states to elements of spoken language. The algorithm is based on the approach accepted in cognitive linguistics stating that there is a causal relationship between body and speech. The novelty of the algorithm is that the total sensory embeddedness of cognitive functions can be evaluated by the algorithm and this is also true for the cognitive functions considered to be impersonal. In contrast to semantic and statistical approaches, the algorithm analyzes objects and functions in language based on universal sensory functions and their associated data, such as universal human functions and sensory data. The proposed algorithm assigns a new codomain to language signals whose data is capable of defining meaning and sensory patterns that have so far been difficult to identify, such as metaphorical and metonymic functions of the language.