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A B S T R A C T The purpose of this study is to develop an issue-oriented automatic syllabus categorization system, in whicn natura l language processing and machine learning based automatic categorization are combined. Recent explosion of scientific knowledge due to the rapid advancement of academia and society makes i t di f f icul t for learners and educators to recognize overall picture of syllabus. I n addit ion, the growing number of interdiscipl inary researches makes i t harder for learners to f ind thei r proper subjects from the syllabi. I n an attempt to present clear directions to their subjects, issue-oriented syllabus structure is expected to be more efficient in learning and education. However, i t normal ly requires categorizing al l the syllabi manual ly i n advance, and i t is generally a t ime consuming task. Thus, this emphasizes the importance of developing efficient methods for (semi-) automatic syllabus st ructur ing in order to accelerate syllabus retr ieval. I n this paper, we introduce design and implementat ion of an issue-oriented automatic syllabus categorization system. And pre l iminary experiments using more than 850 engineering syllabi of Univers i ty of Tokyo show that our proposed syllabus categorization system obtains sufficient accuracy. Keywo rds : automatic te rm recognition, machine learning, text categorization, s t ruc tur ing knowledge

Design and Implementation of an Issue-oriented Automatic Syllabus Categorization Svstem