Natural Language Understanding (NLU) is a complex open problem. NLU complexity is mainly related to semantics: abstraction, representation, real meaning, and computational complexity. While existing approaches can solve some specific problems, they do not address Natural Language problems in a natural way. This invention describes a Semantic Engine using Brain-Like approach (SEBLA) that uses Brain-Like algorithms to solve the key NLU problem (semantics and its sub-problems). The main theme of SEBLA is to use each word as an object with all important features, most importantly the semantics. The next main theme is to use the semantics of each word to derive the meaning of a sentence as we do as humans. Similarly, the semantics of sentences are used to derive the meaning of a paragraph. The 3rd main theme is to use natural semantics as opposed to existing 'mechanical semantics' used in Predicate logic, Ontology or the like.
NATURAL LANGUAGE UNDERSTANDING USING BRAIN-LIKE APPROACH: SEMANTIC ENGINE USING BRAIN-LIKE APPROACH (SEBLA) DERIVES SEMANTICS OF WORDS AND SENTENCES