Deep Semantics in an NLP Knowledge Base

In most natural language processing systems there is no representation of the semantic knowledge of lexical units, but just subcategorization frames, selection restrictions and links to other paradigmatically-related lexical units. Some NLP systems, e.g. machine translation or dialogue-based systems, attempt to “understand” the input text by translating it into some kind of formal language-independent representation; this approach requires a knowledge base with conceptual representations which reflect the structure of human beings’ cognitive system. Even those systems in which surface semantics could be sufficient (e.g. automatic indexing or information extraction), the construction of a robust knowledge base guarantees its use in most natural language processing tasks, consolidating thus the concept of resource reuse. The objective of this paper is to highlight the advantages of storing conceptual meaning representations, and more particularly those in FunGramKB, instead of describing lexical meaning via semantic relations between lexical units.