Automatic Syllabus Classification

Syllabi are important educational resources. However, searching for a syllabus on the Web using a generic search engine is an errorprone process and often yields too many non-relevant links. In this paper, we present a syllabus classifier to filter noise out from search results. We discuss various steps in the classification process, including class definition, training data preparation, feature selection, and classifier building using SVM and Na¨ıve Bayes. Empirical results indicate that the best version of our method achieves a high classification accuracy, i.e., an F1 value of 83% on average.

Main Author: Yu, Xiaoyan
Other Authors: Tungare, Manas, Fan, Weiguo, Perez-Quinones, Manuel, Fox, Edward, Cameron, William, Teng, GuoFang, Cassel, Lillian
Format: Villanova Faculty Authorship
Language: English
Published: 2007
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:175143
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author Yu, Xiaoyan
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Tungare, Manas
Fan, Weiguo
Perez-Quinones, Manuel
Fox, Edward
Cameron, William
Teng, GuoFang
Cassel, Lillian
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Tungare, Manas
Fan, Weiguo
Perez-Quinones, Manuel
Fox, Edward
Cameron, William
Teng, GuoFang
Cassel, Lillian
author_s Yu, Xiaoyan
spellingShingle Yu, Xiaoyan
Automatic Syllabus Classification
author-letter Yu, Xiaoyan
author_sort_str Yu, Xiaoyan
author2 Tungare, Manas
Fan, Weiguo
Perez-Quinones, Manuel
Fox, Edward
Cameron, William
Teng, GuoFang
Cassel, Lillian
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Fan, Weiguo
Perez-Quinones, Manuel
Fox, Edward
Cameron, William
Teng, GuoFang
Cassel, Lillian
dc_title_str Automatic Syllabus Classification
title Automatic Syllabus Classification
title_short Automatic Syllabus Classification
title_full Automatic Syllabus Classification
title_fullStr Automatic Syllabus Classification
title_full_unstemmed Automatic Syllabus Classification
collection_title_sort_str automatic syllabus classification
title_sort automatic syllabus classification
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description Syllabi are important educational resources. However, searching for a syllabus on the Web using a generic search engine is an errorprone process and often yields too many non-relevant links. In this paper, we present a syllabus classifier to filter noise out from search results. We discuss various steps in the classification process, including class definition, training data preparation, feature selection, and classifier building using SVM and Na¨ıve Bayes. Empirical results indicate that the best version of our method achieves a high classification accuracy, i.e., an F1 value of 83% on average.
publishDate 2007
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dc.title Automatic Syllabus Classification
dc.creator Yu, Xiaoyan
Tungare, Manas
Fan, Weiguo
Perez-Quinones, Manuel
Fox, Edward
Cameron, William
Teng, GuoFang
Cassel, Lillian
dc.description Syllabi are important educational resources. However, searching for a syllabus on the Web using a generic search engine is an errorprone process and often yields too many non-relevant links. In this paper, we present a syllabus classifier to filter noise out from search results. We discuss various steps in the classification process, including class definition, training data preparation, feature selection, and classifier building using SVM and Na¨ıve Bayes. Empirical results indicate that the best version of our method achieves a high classification accuracy, i.e., an F1 value of 83% on average.
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