Use Hownet Natural Language Processing (computer)

NLP(Natural Language Processing), especially Chinese Language Processing, is always a difficult problem in computer science. It is a serious challenge for us to make computer to understanding and processing the natural language. NLP is a cross-fields science. Many scientist which from computer science, linguistics, mathematics and many other fields join its researching. The NLP is how to make computer understanding and creating the words in people's nature language, i.e Chinese, English, to make computer understanding the nature language and can communicate with people in nature language, i.e answer the people's question. In recent 10 years, with rapidly developing of computer science and information highway, Natural Language Processing had paid more attention to study semantic and build a large-scale semantic dictionary or a large-scale knowledge-base. The Founder of Hownet , Mr. Dong Zhen-dong, who had taken many years in this field, exploit a new way in chinese information processing, introducing a novel concept: Hownet. Hownet is a bilingual general knowledge-base describing relation between concepts and relations between the attributes of concepts. It provides a enrichment resource for Language Processing.This dissertation mainly discusses how to design and build a knowledge-base, using Hownet theory and the data which Hownet provides. And it also discuss how the knowledge-base can help us for Chinese language processing. The knowledge-base is the nature language denotation system. We use the OOP programe plus database to design it. In detail, we use OOP to descibe the concept classifying of nature language and database to restore the relation of word and its program. So that, computer can parse the collection of some objects that can descibe the meaning of the word rather than the meaningless codes of nature language words. We also attempt to make a knowledge-base API(Application Programming Interface ), in order to service for Chinese semantic disambiguation and Chinese semantic similarity algorithm. At last, dissertation points out the draw back of our program and submits several possible solutions.