Entity Linking among Categorized Knowledge Resources for Computer Science Curricula
DOI:
https://doi.org/10.52731/lir.v003.152Abstract
In educational information processing, both humans and computers require more effective information access than ever. However, there is one serious issue in text processing in higher education. The quantity of digitized text resources is not sufficient to develop large language models in specialized areas. General purpose language models often create unreliable results when the training data do not cover the target domain. To deal with this problem, our proposed method is designed to establish links between lecture course information and entities for real world knowledge. Owing to this entity linking, the meaning of text data can be more easily understood by humans and computers. Our method employs curriculum standards and university syllabus information to associate important keywords with text entities defined in Wikipedia articles. The results of evaluation experiments suggest the effectiveness of the proposed method in feature selection and entity linking for educational information. The contributions of this study include detailed comparison among dictionaries in Japanese morphological analysis. Our findings are expected to provide useful insights for researchers engaging in educational data analysis.
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