Natural Language is what we use as an everyday means of communication among humans. English, Spanish and French are examples of Natural Languages. They have a syntax and grammar, and they comply with principles of economy and optimality, although may contain many ambiguities. They have evolved together with humankind: humans have created all Natural Languages, but no particular human has created any Natural Language.
Oppositely, Formal Languages are those used to transfer information, where no ambiguity is possible. The Math notation, XML, SQL and PHP are examples of these Formal Languages.
Computers can deal with Formal Languages very efficiently, but one of the biggest challenges in computer science is the creation of computers which are able to understand Natural Language. For that purpose, there is a whole field within computer science concerned with the interactions between computers and human (natural) languages: Natural Language Processing (NLP).
Theoretical Linguistic Frameworks like the Meaning–Text Theory (MTT) for the construction of models of natural language, have allowed computers to process natural language, and start understanding the meaning the underneath human language.
Thanks to the use of these NLP theoretical frameworks and computer models, Inbenta has been able to create the Semantic Search Engine, which allow their users to efficiently search for complex information using incomplete, ambiguous, unstructured questions in their own [natural] language.
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