| Summary: | 碩士 === 國立政治大學 === 資訊科學學系 === 92 === With the explosion of World Wide Web, information extraction has become a major technical area. The goal of information extraction is to transform non-structured text into structured data of specific topic. It involves analyzing, filtering and extracting relevant parts of text and the corresponding meaning. Most information extraction research mainly focuses on English text. On the other hand, research on Chinese information extraction has not received as much attention. Considering the fact that one-fifth population in the world are Chinese-speaking people, Chinese information extraction technology will become increasingly important.
Chinese language is different with English in many aspects. In English, words are separated with space such that computers can easily distinguish each word in the input string. In Chinese, there are no spaces between characters to segment them into meaningful words. A general solution is to match characters of the input string to the words in the dictionary to find proper word boundary. Yet, much flexibility and ambiguity exist in the combination of characters into words. Many errors may occur in word segmentation. . In this thesis, we propose an approach to Chinese information extraction based on pattern matching and finite state automata, without relying on word segmentation and part-of-speech tagging. The approach was evaluated with “government personnel directives in official gazettes” as test data, and it achieved performance measure of 98% precision and 97% recall. Moreover, the approach was extended to other data domains. The results have showed initial progress on the research of multiple- domain Chinese information extraction system.
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