@inproceedings{tseng-etal-2018-multilingual,
title = "Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration",
author = "Tseng, Yuen-Hsien and
Lee, Lung-Hao and
Chien, Yu-Ta and
Chang, Chun-Yen and
Li, Tsung-Yen",
editor = "Tseng, Yuen-Hsien and
Chen, Hsin-Hsi and
Ng, Vincent and
Komachi, Mamoru",
booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3723",
doi = "10.18653/v1/W18-3723",
pages = "157--164",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration
%A Tseng, Yuen-Hsien
%A Lee, Lung-Hao
%A Chien, Yu-Ta
%A Chang, Chun-Yen
%A Li, Tsung-Yen
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F tseng-etal-2018-multilingual
%X 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.
%R 10.18653/v1/W18-3723
%U https://aclanthology.org/W18-3723
%U https://doi.org/10.18653/v1/W18-3723
%P 157-164
Markdown (Informal)
[Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration](https://aclanthology.org/W18-3723) (Tseng et al., NLP-TEA 2018)
ACL