:Automatically translate text from one human language to another. This is one of the most difficult problems, and is a member of a class of problems colloquially termed "[[AI-complete]]", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) to solve properly.
; [[Natural- language understanding]] (NLU): Convert chunks of text into more formal representations such as [[first-order logic]] structures that are easier for [[computer]] programs to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural language semantics without confusions with implicit assumptions such as [[closed-world assumption]] (CWA) vs. [[open-world assumption]], or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.<ref>{{cite journal|last1=Duan|first1=Yucong|last2=Cruz|first2=Christophe|year=2011|title=Formalizing Semantic of Natural Language through Conceptualization from Existence|url=http://www.ijimt.org/abstract/100-E00187.htm|journal=International Journal of Innovation, Management and Technology|volume=2|issue=1|pages=37–42|archive-url=https://web.archive.org/web/20111009135952/http://www.ijimt.org/abstract/100-E00187.htm|archive-date=2011-10-09}}</ref>
; [[Natural language generation|Natural-language generation]]<nowiki> (NLG):</nowiki>
:Convert information from computer databases or semantic intents into readable human language.
