The Capabilities and Limitations of
Current Practical Applications of Computational Linguistics
Although the field of Computational Linguistics is relatively new, it contains several sub-areas reflecting practical applications in the field. For example, automatic translation is one of the main components of Computational Linguistics. It can be considered as an independent subject because people who work on this domain are not necessarily experts in the other domains of CL. However, what connects them is the fact that all of these subjects use computers as a tool to deal with human language. Therefore, some people call it Natural Language Processing (abbreviated to NLP). This paper tries to highlight the main field (Computational Linguistics) through some of the essential sub-areas. The capabilities and limitations of each sub-topic will be considered, and possibilities for future development discussed.
A brief history
There is no doubt that scientists have paid attention to Machine (or Automatic) Translation as one of the main computational-linguistics domains. Slocum (1984) states that the development of MT programmes started in the 1950’s. The first applications such as Systran and Meteo were able to translate word by word. The research in machine translation has developed significantly in the late eighties which included morphological and grammar analysis. Hutchins (1993) mentions that one of the important project was the multilingual CICC (Center for the International Cooperation for Computerization) project which involved MT groups in China Indonesia and Thailand. Beginning of the nineties was a major turning point when IBM published the results of its experiments on the system (Candide) based on a statistical method. Machine Translation applications, which has grown rapidly, became available with words processing programs or separate, and also available on the Internet. AppTek, Sakhr, Coltec, ATA and other companies were the first which started their work in the field of machine translation in Arabic language. However, with all these research efforts, machine translation did not reach the level required because of the difficulties faced researchers. There are two types of Automation Tradeoffs:...