Building Dictionaries as Basic Tools for Machine Translation in Natural Language Processing Applications

Natural Language Processing (NLP) software represented in text or voice makes communication possible among people of different languages using their mother tongue. This paper describes the research progress in NLP, machine translation, and electronic language dictionaries commonly used for such communication among people and Internet users around the world who speak different languages. The problems of quality and accuracy in computer translation encountered in both stand-alone and web-based translation systems are described. The possible programming for electronic dictionary as basic tool for NLP applications is also put forward. This involves the creation of software, which will analyze and synthesize language based on the semantic representation of sentences and phrases, and still post considerable challenges to both computational linguist and computer scientists. processing result sometimes is not accurate enough (Grishman, 1989). With the cyberspace and Internet serving as the global information infrastructure, NLP is the medium that facilitates communication within this infrastructure. In this way, NLP can connect and even improve communication among all kinds of human activities. It is our challenge to make NLP system more reliable and easy to understand for many application systems such as, • Multilingual Information Service • Global Information System • Information Retrieval System • Knowledge-based Search Engine • Machine Translation System • Intelligent and Expert System. Although research on Natural Language Processing already began in the late 1940s, serious research activities on this subject are still being pursued in some countries for its own language. In 1980s, CICC (The Center of International Cooperation for Computerization) in Japan developed translation machine with “interlingua” approach. This translation system was not only one-to-one translation, but also capable for multilingual translation. The system developed in interlingua model covered five languages in the system: Indonesian/Malay, Thai, Chinese, Japanese as well as English. These all five languages can be set as either source or target language, and be connected each by English as interlingua base (central system). Lately, CICC interlingua considered also German and French to enable CICC interlingua developed not only to be Asiaversal (Uchida and Zu, 1990:31). The interlingua developed by CICC is different from another interlingua system such as ATLAS II, KMBT, PIVOT, etc. First, this model was developed to handle multilingual translation by computer. For this reason, each concept should be formulated as clear as possible in standardized forms. Secondly, CICC interlingua tries to describe semantic and pragmatic aspect of sentence with certain information category. To achieve this purpose, the system was not oriented on particular linguistics theory although certain linguistics terminology is still being used. On one hand, interlingua must contain all universal features of its member languages, but it must also maintain its simplicity to avoid cumbersome and unmanageable size. There is also the fact that natural language phenomenon is fully marked with variation. For this purpose, interlingua needs to be richer with rules compared to each individual language, not only in grammatical rules, but also in semantic and syntactic relation in between the languages. With five languages covered by the system in CICC, the interlingua should include the rules of all five languages and a set of correlation rules connecting them. In this case, interlingua system should cover all rules to include definite variation in each language member. So far, CICC interlingua didn’t reach that level yet, and any exceptional variations are still handled manually in pre-editing and post editing phase (Yusuf, 1992). In any interlingua system, developing manmachine interface for certain application involves two main activities. As an example in 2000, P.T. Telkom (Indonesian telecommunication company) tried to develop text-to-speech for Indonesian language. First, P.T. Telkom tried to translate all computer terminology into Indonesian terms. Then, all these terminologies have to be synthesized into correct pronunciation. Almost all of computer terminology is absolutely new for Indonesian language and unfortunately this terminological policy has to go through Malay sound, which is a little bit different from Indonesian language. As consequences, some of Indonesian terminology was relatively longer than the original context and sometimes can’t be accurately understood. Many efforts are still being undertaken to make the translation process more effective.