TY - GEN
T1 - Multilingual Short Text Responses Clustering for Mobile Educational Activities
T2 - ACL 2018 5th Workshop on Natural Language Processing Techniques for Educational Applications, NLPTEA 2018
AU - Tseng, Yuen Hsien
AU - Lee, Lung Hao
AU - Chien, Yu Ta
AU - Chang, Chun Yen
AU - Li, Tsung Yen
N1 - Funding Information:
This study was partially supported by the Ministry of Science and Technology (MOST), Taiwan, R.O.C., under the grant: MOST 105-2221-E-003-020-MY2, MOST 106-2221-E-003-030-MY2, and MOST 107-2634-F-002-019-.
Publisher Copyright:
© 2018 Association for Computational Linguistics.
PY - 2018
Y1 - 2018
N2 - Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.
AB - Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.
UR - http://www.scopus.com/inward/record.url?scp=85075942122&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85075942122
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 157
EP - 164
BT - ACL 2018 - Natural Language Processing Techniques for Educational Applications, Proceedings of the 5th Workshop
PB - Association for Computational Linguistics (ACL)
Y2 - 19 July 2018
ER -