Chi-Ling Lee

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2021

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Improved Text Classification of Long-term Care Materials
Yi Fan Chiang | Chi-Ling Lee | Heng-Chia Liao | Yi-Ting Tsai | Yu-Yun Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

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.
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