Computational  linguistics (CL) is a discipline between linguistics and computer science which is concerned with the computational aspects of the human language faculty. It belongs to the cognitive sciences and overlaps with the field of artificial intelligence (AI), a branch of computer science aiming at computational models of human cognition. Computational linguistics has applied and theoretical components.[1]

     Theoretical CL takes up issues in theoretical linguistics and cognitive science. It deals with formal theories about the linguistic knowledge that a human needs for generating and understanding language. Today these theories have reached a degree of complexity that can only be managed by employing computers. Computational linguists develop formal models simulating aspects of the human language faculty and implement them as computer programmes. These programmes constitute the basis for the evaluation and further development of the theories. In addition to linguistic theories, findings from cognitive psychology  play a major role in simulating linguistic competence. Within psychology, it is mainly the area of psycholinguistics that examines the cognitive processes constituting human language use. The relevance of computational modeling for psycholinguistic research is reflected in the emergence of a new subdiscipline: computational psycholinguistics.[2]

     Applied CL focuses on the practical outcome of modeling human language use. The methods, techniques, tools and applications in this area are often subsumed under the term  language engineering or (human) language technology.  Although existing CL systems are far from achieving human ability, they have numerous possible applications. The goal is to create software products that have some knowledge of human language. Such products are going to change our lives. They are urgently needed for improving human-machine interaction since the main obstacle in the interaction between human and computer is a communication problem. Today's computers neither  understand our language but computer languages are difficult to learn nor  correspond to the structure of human thought. Even if the language that machine understands and its domain of discourse are very restricted, the use of human language can increase the acceptance of software and the productivity of its users. [3]

     Computational linguistics can be divided into major areas depending upon the medium of the language being processed, whether spoken or textual; and upon the task being performed, whether analyzing language (recognition) or synthesizing language (generation).[5]

     Speech recognition and speech synthesis deal with how spoken language can be understood or created using computers. Parsing and generation are sub-divisions of computational linguistics dealing respectively with taking language apart and putting it together. Machine translation remains the sub-division of computational linguistics dealing with having computers translate between languages. Some of the areas of research that are studied by computational linguistics include:

•·  Computational complexity of natural language, largely modeled on automata theory, with the application of context-sensitive grammar and linearly-bounded Turing machines.

•·  Computational semantics comprises defining suitable logics for linguistic meaning representation, automatically constructing them and reasoning with them

•·  Design of taggers like POS-taggers (part-of-speech taggers)

•·  Machine translation as one of the earliest and most difficult applications of computational linguistics draws on many subfields.[6]

       The most  famous researchers in this field are: Igor Bolshakov, Alexander Gelbukh, Ralph Grishman, Hans Uszkoreit, Ronald Hausser, John Hutchins, Daniel Jurafsky and others.