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    <abstract>Aging populations have posed a challenge to many countries including Taiwan, and with them come the issue of long-term care. Given the current context, the aim of this study was to explore the hotly-discussed subtopics in the field of long-term care, and identify its features through NLP. This study applied TF-IDF, the Logistic Regression model, and the Naive Bayes classifier to process data. In sum, the results showed that it reached a best F1-score of 0.920 in identification, and a best accuracy of 0.708 in classification. The results of this study could be used as a reference for future long-term care related applications.</abstract>
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%0 Conference Proceedings
%T Improved Text Classification of Long-term Care Materials
%A Chiang, Yi Fan
%A Lee, Chi-Ling
%A Liao, Heng-Chia
%A Tsai, Yi-Ting
%A Chang, Yu-Yun
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F chiang-etal-2021-improved
%X Aging populations have posed a challenge to many countries including Taiwan, and with them come the issue of long-term care. Given the current context, the aim of this study was to explore the hotly-discussed subtopics in the field of long-term care, and identify its features through NLP. This study applied TF-IDF, the Logistic Regression model, and the Naive Bayes classifier to process data. In sum, the results showed that it reached a best F1-score of 0.920 in identification, and a best accuracy of 0.708 in classification. The results of this study could be used as a reference for future long-term care related applications.
%U https://aclanthology.org/2021.rocling-1.38/
%P 294-300

Markdown (Informal)

[Improved Text Classification of Long-term Care Materials](https://aclanthology.org/2021.rocling-1.38/) (Chiang et al., ROCLING 2021)

• Improved Text Classification of Long-term Care Materials (Chiang et al., ROCLING 2021)

ACL

• Yi Fan Chiang, Chi-Ling Lee, Heng-Chia Liao, Yi-Ting Tsai, and Yu-Yun Chang. 2021. Improved Text Classification of Long-term Care Materials. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 294–300, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).