Abstract
The purpose of this study is to develop an issue-oriented automatic syllabus categorization system, in which natural 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 it difficult for learners and educators to recognize overall picture of syllabus. In addition, the growing number of interdisciplinary researches makes it harder for learners to find their proper subjects from the syllabi. In an attempt to present clear directions to their subjects, issue-oriented syllabus structure is expected to be more efficient in learning and education. However, it normally requires categorizing all the syllabi manually in advance, and it is generally a time consuming task. Thus, this emphasizes the importance of developing efficient methods for (semi-) automatic syllabus structuring in order to accelerate syllabus retrieval. In this paper, we introduce design and implementation of an issue-oriented automatic syllabus categorization system. And preliminary experiments using more than 850 engineering syllabi of University of Tokyo show that our proposed syllabus categorization system obtains sufficient accuracy.
