The cornerstone of current Computational Linguistics research is the computation of semantic similarity between lexical items or some of their conceptualization in available semantic resources such as WordNet. However, measures for semantic similarity (and/or relatedness) usually work with numerical outputs, which are then used to solve tasks related to word disambiguation rather than information retrieval. In this paper, we start from the limitations of using numeric-based similarity measures, proposing a novel approach to provide explanations of similarity, even if still calculated through statistical (and thus numerical) analyses. This may allow a novel, fine-grained and context-based similarity reasoning over lexical entities. In this contribution, we define the concept of semantic similarity reasoning and a method of extraction from ConceptNet, a large common-sense resource. Finally, we present a number of hypotheses of how such shift of paradigm could represent a new building block of future natural language technologies. © Springer International Publishing Switzerland 2016.
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