| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 99 === A lot of digital data have been accumulated in many enterprises and institutions due to the continuing advanced in information technology. These data reflect human being''s accumulation of all knowledge of specialized territories. According to the users'' demands for categorizing documents, users classify those documents depend on their related experiences. Consequently, they categorize the daily record according to every department''s related experiences. However, these daily record contain many technical terms. SNR(Signal to Noise Ratio), DNS(Domain Name System), and CM(Cable Modem) are the examples of technical terms. Therefore, users can classify these daily records into the proper catalogues quickly and correctly by using the system''s document automatic classification. This study analyzes the relationship between the term and it depth by applying the ontology, and then examine the results of classification by using different classification methods. Moreover, using the neural network model to categorize the documents automatically. Using the precision recall, and F1 to evaluate the effects of the classification that cooperated with the neural network. The study represented that the result of using term presence(TP) which cooperated with neural network had more positive classification effectiveness than the trainditional method and TF-IDF. The result of this study can use the techniques of TP to enhance the precision of automatic classification for daily record according to the key term depth. We will collect departments’ even records will be collected to enhance the effectiveness of automatic classification in the future.
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