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3 year student, Oksana Kychko
NTUU "KPI", Department of LinguisticsKyiv
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.
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.
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. 
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).
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:
•· 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.
The most famous researchers in this field are: Igor Bolshakov, Alexander Gelbukh, Ralph Grishman, Hans Uszkoreit, Ronald Hausser, John Hutchins, Daniel Jurafsky and others.
Natural language interfaces enable the user to communicate with the computer in French, English, German, or another human language. Some applications of such interfaces are database queries, information retrieval from texts ( expert systems) and robot control. Current advances in the recognition of spoken language improve the usability of many types of natural language systems. Communication with computers using spoken language will have a lasting impact upon the work environment, completely new areas of application for information technology will open up. However, spoken language needs to be combined with other modes of communication such as pointing with mouse or finger. If such multimodal communication is finally embedded in an effective general model of cooperation, we have succeeded in turning the machine into a partner. Much older than communication problems between human beings and machines are those between people with different mother tongues. One of the computational linguistics' original targets has always been fully automatic translation between human languages. Nevertheless, computational linguists have created software systems that simplify the work of human translators and clearly improve their productivity. Less than perfect automatic translations can also be of great help to information seekers who have to search through large amounts of texts in foreign languages.
The rapid growth of the Internet/WWW and the emergence of the information society poses exciting new challenges to language technology. Although the new media combine text, graphics, sound and movies, the whole world of multimedia information can only be structured, indexed and navigated through language. For browsing, navigating, filtering and processing the information on the web, we need software for the contents of documents. Language technology for content management is a necessary precondition for turning the wealth of digital information into collective knowledge. The increasing multilinguality of the web constitutes is an additional challenge for this discipline. The global web can only be mastered with the help of multilingual tools for indexing and navigating. Systems for crosslingual information and knowledge management will surmount language barriers for e-commerce, education and international cooperation.
Nowadays mechanisms that underlie human language processing keeps growing. Modeling such mechanisms on a computer also helps us to discover and formally describe hidden properties of human language that are relevant for any kind of language processing including many useful software applications.
So, CL long-term goal is the deep understanding of human language and powerful intelligent linguistic applications.
•1. Bolshakov I., Gelbukh A. Computational linguistics:models, resources, applications .Mexico, 2004,186 рр.
•2. Grishman, R. Computational linguistics. An introduction. Cambridge University Press, 1986.
•3. Hans Uszkoreit. What Is Computational Linguistics? Department of Computational Linguistics and Phonetics of Saarland University
•4. Hausser, Ronald. Foundations of computational linguistics: man-machine communication in natural language. Springer Verlag, 1999.
•5. Jurafsky, D., J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice-Hall, 2000
•6. John Hutchins: Retrospect and prospect in computer-based translation. Proceedings of MT Summit VII, 1999, pp. 30-44.