. Machine Translation & Information Extraction
Natural Language Processing (NLP) is the automatic analysis of human languages such as English, Korean, etc. by computer algorithms. Unlike artificially created programming languages where the structure and meaning of programs is easy to encode, human languages provide an interesting challenge, both in terms of its analysis and the learning of language from observations.
NLP can be used for the transduction of one linguistic form to another or parsing of language into a structured form. Transduction of language involves summarizing, paraphrasing or translating languages. Parsing involves conversion of unstructured data into a structured form, such as speech into text or large text collections like the web into informative labels. Examples of parsing include identifying a group of words as a person's name or identifying the recursive grammar of a language.
Statistical machine translation and language independent acoustic modeling were two of the topics studied at the 1999 Johns Hopkins University Language Engineering Workshop hosted by the Center for Language and Speech Processing. In booth of these topics Czech played the role of the target experimental language.

Automatic translation from one human language to another using computers, better known as machine translation (MT), is a longstanding goal of computer science.
Recently, statistical data analysis has been used to gather MT knowledge automatically from parallel bilingual text. Unfortunately, these techniques and tools have not been disseminated to the scientific community in very usable form, and new follow-on ideas have developed sporadically.