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Publications

https://scholar.google.com/citations?user=vD5bO5sAAAAJ&hl=zh-TW

​Journals

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  1. Yan Z., Lu S., Xu D., Yang Y., Wang H., Mao J., Fan Y., Chen Y.,  & Tseng H. C. (2024). P717 Evaluating the performance of Large Language Models in responding to patients' health queries: A comparative analysis with medical experts, Journal of Crohns and Colitis, 18(Supplement_1), i1348–i1349, https://doi.org/10.1093/ecco-jcc/jjad212.0847 (SCIE)

  2. 徐于惠, 杨会敏, 冯晓晴, 曾厚強, 張道行, 李佳, 刘晓曼, 黄民, 陈孝, & 郑逸凡. (2023). 非小细胞肺癌药物治疗相关中文网页类患者教育材料质量和科普价值评价 Quality and Educational Value Evaluation of Patient Education Materials Related to Drug Therapy of Non-small Cell Lung Cancer in Chinese Websites. 今日药学 Pharmacy Today, 33(5), 380-385.

  3. Zheng, Y., Tang, Y., Tseng, H. C., Chang, T. H., Li, L., Chen, P., ... & Tang, K. J. (2022). Evaluation of quality and readability of over-the-counter medication package inserts. Research in Social and Administrative Pharmacy, 18(9), 3560-3567. https://doi.org/10.1016/j.sapharm.2022.03.012 (SSCI)

  4. Sung, Y.-T., Cheng, H.-H., Tseng, H.-C., Chang, K.-E., & Lin, S.-Y. (2024). Construction and validation of a computerized creativity assessment tool with automated scoring based on deep-learning techniques. Psychology of Aesthetics, Creativity, and the Arts, 18(4), 493–509. https://doi.org/10.1037/aca0000450

  5. Chen, C. C., Sung, Y. T., Chang, C. F., & Tseng, H. C. (2020). Examining the differences of readability leveling of chinese popular science books by experts and by crie system for elementary school children. Journal of Library & Information Studies, 18(1), 45-67. (TSSCI)

  6. Hong, J. F., Peng, C. Y., Tseng, H. C., & Sung, Y. T. (2020). Linguistic feature analysis of cefr labeling reliability and validity in language textbooks. Journal of Technology and Chinese Language Teaching, 11(1), 57-83. (Scopus)

  7. Tseng, H. C., Chen, B., Chang, T. H., & Sung, Y. T. (2019). Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts. Natural Language Engineering, 25(3), 331-361. (SSCI; SCIE)

  8. 陳茹玲、曾厚強、宋曜廷、林慶隆、柯華葳（2017）。華語文教材之文本分析與可讀性研究。國際中文教育學報，1期，39-71。

  9. Tseng, H. C., Chen, B., & Sung, Y. T. (2017). Exploring the Use of Neural Network based Features for Text Readability Classification. International Journal of Computational Linguistics and Chinese Language Processing. 22(2), 31-46.

  10. Sung, Y. T., Chen, J. L., Cha, J. H., Tseng, H. C., Chang, T. H., & Chang, K. E. (2015). Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning. Behavior research methods 47(2), 340-354. (SSCI)

  11. 宋曜廷、陳茹玲、李宜憲、查日龢、曾厚強、林維駿、張道行、張國恩。(2013)。中文文本可讀性探討：指標選取、模型建立與效度驗證。中華心理學刊，55(1)，75-106。(TSSCI)

  12. Tsai, H. H., Tseng, H. C., & Lai, Y. S. (2010). Robust lossless image watermarking based on α-trimmed mean algorithm and support vector machine. Journal of Systems and Software, 83(6), 1015-1028.

​International Conferences

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  1. Hui-Xin Chen, Hou-Chiang Tseng & Yao-Ting Sung. (2024). Analysis of the Readability of Health Education Texts for Elderly Readers: An Eye-Tracking Experiment. 2024 Conference on The 16th Asian Conference on Education (ACE2024), Japan.

  2. Yi-Ju Chan, Hou-Chiang Tseng & Yao-Ting Sung. (2024). Use eye movement features to explore the impact of language features on text difficulty. 2024 Conference on The 16th Asian Conference on Education (ACE2024), Japan.

  3. Lo, M. C., Chang, L. Y., & Tseng, H. C.* (2024). Extracting the semantic representation of Chinese-Japanese homophones with Word2Vec for teaching Chinese as a second/foreign language. International Conference of Innovative Technologies and Learning, Tartu, Estonia, August 13th-16th, 2024.

  4. Chang, L. Y., Haung, J. E., Lo, M. C., & Tseng, H. C.* (2024). CD-ROM in Chinese character education: Streamlining material complication with multi-level features. International Conference of Innovative Technologies and Learning, Tartu, Estonia, August 13th-16th, 2024.

  5. Tseng, H. C., Sung, Y. T., & Chen, B. (2021). Effective text readability classification with unsupervised knowledge injection. The 8th International School Chinese Language Education Conference and Workshop (ISCLE-8), TAIPEI, TAIWAN.

  6. Tseng, H. C., Chen, H. C., Chang, K. E. Sung, Y. T., & Chen B. (2019). An Innovative BERT-Based Readability Model. The 2019 Conference on International Conference of Innovative Technologies and Learning (ICITL 2019). Tromsö, Norway. (Oral)

  7. Sung, Y. T., Hong, J.-F., & Tseng, H. C. (2018). A Linguistic Feature Analysis of CEFR Labeling in Language Textbooks. 6th International Workshop on Advanced Learning Sciences (IWALS 2018), Pittsburgh, PA, USA. (Oral)

  8. Tseng, H. C., Sung, Y. T., & Chen, B. (2018). An innovative readability model developed by neural network-based features. 6th International Workshop on Advanced Learning Sciences (IWLAS 2018), Pittsburgh, PA, USA. (Oral)

  9. Sung, Y. T., Tseng, H. C., Chang, K. N., Chen, H. C., & Hsiao, H. S. (2016). Beyond Linguistics Features: Readability Evaluation for Domain-Specific Texts. International Conference on the Globalization of Second Language Acquisition and Teacher Education, Fukuoka, Japan.

  10. Tseng, H. C., Sung, Y. T., & Chen, B. (2016). Classification of Text Readability Based on Representation Learning Techniques. 26th Annual Meeting of the Society for Text & Discourse (ST&D 2016). Kassel. Germany. (Poster)

  11. Tseng, H. C., Chang, T. H., Chen, B., & Sung, Y. T. (2014). Analyzing Textbooks by a Readability Model Based on Concepts and Support Vector Machine. The 4th Annual Asian Conference on Language Learning (ACLL 2014), Osaka, Japan. (Poster)

  12. Lee, Y. L., Tseng, H. C., Sung, Y. T., Chen, J. L., & Shu, S. H. (2014). The Readability of Diabetes Patient Education Materials on the World Wide Web based on LSA and SVM technique. The American Medical Informatics Association (AMIA), Washington. (Poster)

  13. Hong, J. F., Tseng, H. C., Li, Y. S., & Sung, Y.T. (2012). Constructing a Chinese text readability formula with multi-level linguistic features. The 42nd Annual Meeting of the Society for Computers in Psychology (SCiP 2012), Minneapolis, MN. (Poster)

  14. Tseng, H. C., Chang, T. H., & Sung, Y. T. (2012). Evaluation of the feasibility of online readability application. The 42nd Annual Meeting of the Society for Computers in Psychology (SCiP 2012), Minneapolis, MN. (Poster)

  15. Lee, Y. S., Tseng, H. C., Chen, J. L., Peng, C. Y. Chang, T. H., & Sung, Y. T. (2012). Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. The 12th IEEE International Conference on Advanced Learning Technologies (ICALT), Rome, Italy. (Poster)

  16. Sung,Y. T., Chen, J. L., Lee,Y. T., Lee, Y. S., Peng, C. Y., Tseng, H. C., & Chang, T. H. (2012). Constructing and validating a readability modal with LSA : A case Study of Chinese and social science textbooks. The 22th Annual Meeting of Society for Text and Discourse Process, Montreal, Canada.

  17. Chen, J. L., Tseng, H. C., Cha, J. H., Hong, J.F., Chang, T. H., & Sung, Y. T. (2012). The construction of readability formula for Chinese text using SVM: the Preliminary study. The 7th International Conference & Workshops on Technology & Chinese Language Teaching (TCLT), HI.

​Local Conferences

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  1. Pin-Hsiu Lin, Hou-Chiang Tseng & Kuan-Yu Chen. (2024). A Chinese Education Broadcast Emotion Corpus. 2024 Conference on Computational Linguistics and Speech Processing (ROCLING 2024), Taiwan.

  2. Chieh-Hsuan Wu, Hou-Chiang Tseng, Kuan-Yu Chen, Berlin Chen & Yao-Ting Sung. (2024). Evaluating the Accuracy of Image Captioning Technology in Enhancing Text Readability Models. The 2024 Conference on Computational Linguistics and Speech Processing (ROCLING 2024), Taiwan.

  3. Hojin Koh, Wan-Jun Gu, Hou-Chiang Tseng, Kuan-Yu Chen & Yao-Ting Sung. (2024). Rewriting Chinese Educational Materials to Change Readability Levels with Large Language Models: Strategies and Challenges. The 2024 Conference on Computational Linguistics and Speech Processing (ROCLING 2024), Taiwan.

  4. 戴采薰、曾厚強、宋曜廷. (2024). 基於Word2vec 語言模型自動評估台灣英文文本可讀性. 2024技職教育永續發展學術研討會– 人工智慧時代的創新與挑戰. 台北, 台灣. 

  5. 鄭羽倢、林嬋娟、曾厚強、宋曜廷. (2023年12月). 企業社會責任報告書之揭露品質會影響機構投資人持股嗎? 2023中華會計教育學會年會. 台北, 台灣.

  6. Tai, T. N., Tseng, H. C., & Sung, Y. T. (2023). Impact of Feature Selection Algorithms on Readability Model. The 2023 Conference on Computational Linguistics and Speech Processing (ROCLING 2023), Taiwan.

  7. Haung, J. E., Tseng, H. C., Chang, L. Y., Chen, H. C., & Sung, Y. T. (2022). The Design and Development of a System for Chinese Character Difficulty and Features. The 2022 Conference on Computational Linguistics and Speech Processing (ROCLING 2022), Taiwan.

  8. Weng S. Y., Tseng, H. C., Sung, Y. T. & Chen B., (2019). A Hierarchical Encoding Framework for Text Readability Prediction. The 2019 Conference on Computational Linguistics and Speech Processing (ROCLING 2019), 334-342. Taiwan. (Poster, Best Poster Presentation Award)

  9. Tseng, H. C., Chen B., & Sung, Y. T. (2018). Exploring Combination of FastText and Convolutional Neural Networks for Building Readability Models. The 2018 Conference on Computational Linguistics and Speech Processing (ROCLING 2018), 116-125. Taiwan. (Oral)

  10. Tseng, H. C., Chen B., & Sung, Y. T. (2017). Exploring Readability Analysis on Multi-Domain Texts. The 2017 Conference on Computational Linguistics and Speech Processing (ROCLING 2017), 116-118. Taiwan. (Oral)

  11. Tseng, H. C., Hung, H. T., Sung, Y. T., & Chen B. (2016). Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques. The 2016 Conference on Computational Linguistics and Speech Processing (ROCLING 2016), 255-270. Taiwan. (Poster)

  12. 曾厚強, 宋曜廷, 陳柏琳（2015）。基於表示學習技術之文件可讀性分類。TAAI2015，246-251。台灣。(Oral)

  13. Liu, Y. N., Chen, K. Y., Tseng, H. C., & Chen B. (2015). A Study of Readability Prediction on Elementary and Secondary Chinese Textbooks and Excellent Extracurricular Reading Materials. The 2016 Conference on Computational Linguistics and Speech Processing (ROCLING 2015), 71–86. Taiwan. (Oral)
     

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