Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach
Authors: 
Jingrui Hou, Shitou ZhangAuthors Info & Claims
Jingrui Hou, Shitou ZhangAuthors Info & ClaimsArticle No.: 60, Pages 1 - 19
Abstract
Classical Chinese poetry, as an essential aspect of cultural heritage, exhibits rich theme diversity often overlooked in natural language processing research. To address this gap, we aim to explore the classification of thematic categories within this literary domain. We curate a dataset of 2,918 annotated poems spanning 7 common themes and propose a BERT-based ensemble learning approach for effective classification. Although this method integrates existing models, it achieves an accuracy and F1 score of over 72% in the 7-class task, surpassing established baselines, and providing a baseline for future research. The experimental findings reveal the effectiveness of ensemble strategies in improving individual base model performance and highlight the potential of the MLP-based ensemble technique. The study contributes to a deeper understanding of thematic categories and textual features in classical Chinese poetry and offers an automated classification system for classical Chinese poems.
References
[1]
Zhou Ai, Zhang Yijia, Wei Hao, and Lu Mingyu. 2021. LDA-Transformer Model in Chinese Poetry Authorship Attribution. In Proceedings of the Information Retrieval. Hongfei Lin, Min Zhang, and Liang Pang (Eds.), Springer International Publishing, Cham, 59–73. DOI:
[2]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR ’15). Yoshua Bengio and Yann LeCun (Eds.), Conference Track Proceedings. Retrieved from http://arxiv.org/abs/1409.0473
[3]
Leo Breiman. 1996. Bagging Predictors. Machine Learning 24 (1996), 123–140. DOI:
[4]
J. Briskilal and C. N. Subalalitha. 2022. An Ensemble Model for Classifying Idioms and Literal Texts Using BERT and RoBERTa. Information Processing & Management 59, 1 (2022), 102756. DOI:
[5]
Zong-Qi Cai. 2018. How to Read Chinese Poetry: A Guided Anthology. Columbia University Press.
[6]
Xinru Cui, Jinxu Qi, Hao Tan, and Feng Chen. 2017. Comparison of Ancient and Modern Chinese Based on Complex Weighted Networks. Plos One 12, 11 (Nov. 2017), 1–14. DOI:
[7]
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020. Revisiting Pre-Trained Models for Chinese Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. Association for Computational Linguistics, Online, 657–668. Retrieved from https://www.aclweb.org/anthology/2020.findings-emnlp.58
[8]
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, and Ziqing Yang. 2021. Pre-Training with Whole Word Masking for Chinese BERT. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (Nov. 2021), 3504–3514. DOI:
[9]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long and Short Papers), Association for Computational Linguistics, 4171–4186. DOI:
[10]
Hantian Ding, Jinrui Yang, Yuqian Deng, Hongming Zhang, and Dan Roth. 2022. Towards Open-Domain Topic Classification. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations. Hannaneh Hajishirzi, Qiang Ning, and Avi Sil (Eds.), Association for Computational Linguistics, 90–98. DOI:
[11]
Cunxiao Du, Zhaozheng Chen, Fuli Feng, Lei Zhu, Tian Gan, and Liqiang Nie. 2019. Explicit Interaction Model towards Text Classification. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conference and 9th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI ’19/IAAI ’19/EAAI ’19). AAAI Press, Article 780, 8 pages. DOI:
[12]
Yoav Freund and Robert E. Schapire. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55, 1 (1997), 119–139. DOI:
[13]
M. A. Ganaie, Minghui Hu, A. K. Malik, M. Tanveer, and P. N. Suganthan. 2022. Ensemble Deep Learning: A Review. Engineering Applications of Artificial Intelligence 115 (2022), 105151. DOI:
[14]
Tian Gao, Shanliang Zhu, Jing Liu, Jun Shen, Jialie Shen, Shuguo Yang, and Pengcheng Xiong. 2021. A New Context-Aware Approach for Automatic Chinese Poetry Generation. Knowledge-Based Systems 232 (2021), 107409. DOI:
[15]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780. DOI:
[16]
Jingrui Hou and Ping Wang. 2023. Assemble the Shallow or Integrate a Deep? Toward a Lightweight Solution for Glyph-Aware Chinese Text Classification. Plos One 18, 7 (2023), e0289204. DOI:
[17]
Yufang Hou and Anette Frank. 2015. Analyzing Sentiment in Classical Chinese Poetry. In Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH ’15). Association for Computational Linguistics, 15–24. DOI:
[18]
Renfen Hu and Yuchen Zhu. 2015. Automatic Classification of Tang Poetry Themes. Acta Scientiarum Naturalium Universitatis Pekinensis 2 (2015), 262–268. DOI:
[19]
Michael I. Jordan. 1997. Chapter 25 - Serial Order: A Parallel Distributed Processing Approach. In Proceedings of the Neural-Network Models of Cognition. John W. Donahoe and Vivian Packard Dorsel (Eds.), Advances in Psychology, Vol. 121. North-Holland, 471–495. DOI:
[20]
Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP ’14). Association for Computational Linguistics, 1746–1751. DOI:
[21]
Sabina Knight. 2012. Chinese Literature: A Very Short Introduction. Oxford University Press.
[22]
Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, and Matthias Steinbrecher. 2022. Multi-Layer Perceptrons. In: Computational Intelligence. Springer International Publishing, Cham, 53–124. DOI:
[23]
Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, and Lifang He. 2022. A Survey on Text Classification: From Traditional to Deep Learning. ACM Transactions on Intelligent Systems and Technology 13, 2, Article 31 (Apr. 2022), 41 pages. DOI:
[24]
AhmedCherif Mazari, Nesrine Boudoukhani, and Abdelhamid Djeffal. 2024. BERT-based ensemble learning for multi-aspect hate speech detection. Cluster Computing 27, 1 (Feb 2024), 325-339. DOI:
[25]
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2021. Deep Learning–Based Text Classification: A Comprehensive Review. Computing Surveys 54, 3, Article 62 (Apr. 2021), 40 pages. DOI:
[26]
Aytuğ Onan. 2018. An Ensemble Scheme Based on Language Function Analysis and Feature Engineering for Text Genre Classification. Journal of Information Science 44, 1 (2018), 28–47. DOI:
[27]
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A Survey on Deep Learning: Algorithms, Techniques, and Applications. Computing Surveys 51, 5, Article 92 (Sep. 2018), 36 pages. DOI:
[28]
Jason Protass. 2021. The Poetry Demon: Song-Dynasty Monks on Verse and the Way. University of Hawaii Press.
[29]
Yizhan Shao, Tong Shao, Minghao Wang, Peng Wang, and Jie Gao. 2021. A Sentiment and Style Controllable Approach for Chinese Poetry Generation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ’21). ACM, New York, NY, 4784–4788. DOI: https://doi.org/10.1145/3459637.3481964
[30]
Yabo Shen, Yong Ma, Chunguo Li, Shidang Li, Mingliang Gu, Chaojin Zhang, Yun Jin, and Yingli Shen. 2019. Sentiment Analysis for Tang Poetry Based on Imagery Aided and Classifier Fusion. In Proceedings of the Artificial Intelligence for Communications and Networks. Shuai Han, Liang Ye, and Weixiao Meng (Eds.), Springer International Publishing, Cham, 283–290.
[31]
Chang Su, Shupin Liu, and Chalian Luo. 2023. MISC: A Multimodal Approach for Sentiment Classification of Classical Chinese Poetry. In Proceedings of the Advanced Intelligent Computing Technology and Applications. De-Shuang Huang, Prashan Premaratne, Baohua Jin, Boyang Qu, Kang-Hyun Jo, and Abir Hussain (Eds.), Springer Nature Singapore, Singapore, 432–442. DOI:
[32]
Yongrui Tang, Xumei Wang, Peng Qi, and Yan Sun. 2020. A Neural Network-Based Sentiment Analysis Scheme for Tang Poetry. In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC ’20), 1783–1788. DOI:
[33]
Huishuang Tian, Kexin Yang, Dayiheng Liu, and Jiancheng Lv. 2021. AnchiBERT: A Pre-Trained Model for Ancient Chinese Language Understanding and Generation. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN ’21). IEEE, 1–8.
[34]
Grigorios Tsoumakas and Ioannis Katakis. 2007. Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining (IJDWM) 3, 3 (2007), 1–13. DOI:
[35]
University of Illinois Library. [n.d.]. Copyright Reference Guide: Public Domain. Retrieved January 4, 2024 from https://guides.library.illinois.edu/c.php?g=46308&p=294952
[36]
Qing Wang, Xiumei Wang, Weiping Liu, and Guannan Chen. 2021. Predicting the Chinese Poetry Prosodic Based on a Developed BERT Model. In Proceedings of the 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE ’21), 583–586. DOI:
[37]
Yousheng Wang. 2010. On Principles of Compilation and Thoughts of Poetic in Selection of the Song Poems. Journal of Central South University (Social Science) 16, 02 (2010), 109–115.
[38]
Chen Xia and Zhou Jing. 2018. English Translation of Classical Chinese Poetry. Orbis Litterarum 73, 4 (2018), 361–373. DOI:
[39]
Jing Xuan, Zhongshi He, Liangyan Li, He Weidong, Fei Guo, Hang Zhang, and Qiong Wu. 2018. Brain-Oriented Cconvolutional Neural Network Computer Style Recognition of Classical Chinese Poetry. Neuroquantology 16, 4 (2018), 107–115. DOI:
[40]
Rui Yan, Han Jiang, Mirella Lapata, Shou-De Lin, Xueqiang Lv, and Xiaoming Li. 2013. I, Poet: Automatic Chinese Poetry Composition through a Generative Summarization Framework under Constrained Optimization. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI ’13). AAAI Press, 2197–2203.
[41]
Chyan Yang and Hsien-Jyh Liao. 2010. Using the Robots.txt and Robots Meta Tags to Implement Online Copyright and a Related Amendment. Library Hi Tech 28, 1 (2010), 94–106. DOI:
[42]
Cheng Yang, Maosong Sun, Xiaoyuan Yi, and Wenhao Li. 2018. Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 3960–3969. DOI:
[43]
Xin Ye, Hongxia Dai, Lu-an Dong, and Xinyue Wang. 2021. Multi-View Ensemble Learning Method for Microblog Sentiment Classification. Expert Systems with Applications 166 (2021), 113987. DOI:
[44]
Wen-Chao Yeh, Yung-Chun Chang, Yu-Hsuan Li, and Wei-Chieh Chang. 2019. Rhyming Knowledge-Aware Deep Neural Network for Chinese Poetry Generation. In Proceedings of the 2019 International Conference on Machine Learning and Cybernetics (ICMLC ’19), 1–6. DOI:
[45]
Hang Zhang and Zhongshi He. 2020. Brain-Oriented Cconvolutional Neural Network Computer Style Recognition of Classical Chinese Poetry. Modern Computer 2, 23 (2020), 12–17. DOI:
[46]
Wei Zhang, Hao Wang, Min Song, and Sanhong Deng. 2023. A Method of Constructing a Fine-Grained Sentiment Lexicon for the Humanities Computing of Classical Chinese Poetry. Neural Computing and Applications 35, 3 (2023), 2325–2346. DOI:
[47]
Xingxing Zhang and Mirella Lapata. 2014. Chinese Poetry Generation with Recurrent Neural Networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP ’14). Association for Computational Linguistics, 670–680. DOI:
[48]
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-Level Convolutional Networks for Text Classification. In Proceedings of the 28th International Conference on Neural Information Processing Systems – Volume 1 (NIPS ’15). MIT Press, Cambridge, MA, 649–657.
[49]
Jiaqi Zhao, Ting Bai, Yuting Wei, and Bin Wu. 2022. PoetryBERT: Pre-Training with Sememe Knowledge for Classical Chinese Poetry. In Proceedings of the International Conference on Data Mining and Big Data. Springer, 369–384. DOI:
[50]
Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, and Xiaoyong Du. 2019. UER: An Open-Source Toolkit for Pre-training Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP ’19): System Demonstrations. Sebastian Padó and Ruihong Huang (Eds.), Association for Computational Linguistics, 241–246. DOI:
[51]
Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, et al. 2023. TencentPretrain: A Scalable and Flexible Toolkit for Pre-Training Models of Different Modalities. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). Danushka Bollegala, Ruihong Huang, and Alan Ritter (Eds.), Association for Computational Linguistics, 217–225. DOI:
[52]
Zhenjie Zhao and Xiaojuan Ma. 2020. ShadowPlay2.5D: A 360-Degree Video Authoring Tool for Immersive Appreciation of Classical Chinese Poetry. Journal on Computing and Cultural Heritage 13, 1, Article 5 (Feb. 2020), 20 pages. DOI:
[53]
Ai Zhou, Yijia Zhang, and Mingyu Lu. 2022. C-Transformer Model in Chinese Poetry Authorship Attribution. International Journal of Innovative Computing, Information and Control 18 (2022), 901–916. DOI:
[54]
Zhi-Hua Zhou. 2015. Ensemble Learning. Springer US, Boston, MA, 411–416. DOI:
[55]
Zhenying Zhuo and Xiaohua Liu. 2010. Translation of Chinese Ancient Poetry (Annotated Bilingual Edition with Pinyin). JiNan University Press.
Index Terms
- Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach
Recommendations
Classical Chinese poetry generation from vernacular Chinese: a word-enhanced supervised approach
AbstractIn recent years, with the rapid development of deep learning, natural language processing has achieved great progress in many aspects. In the field of text generation, classical Chinese poetry, as an important part of Chinese culture, also ...
Translating Classical Chinese Poetry into Modern Chinese with Transformer
Chinese Lexical SemanticsAbstractClassical Chinese poetry, as the cultural heritage of human beings, is very popular in Chinese community all over the world. Nearly every person in these regions can recite several poems to artistically express his or her emotion. However, due to ...
Building Locally Discriminative Classifier Ensemble Through Classifier Fusion Among Nearest Neighbors
PCM 2016: 17th Pacific-Rim Conference on Advances in Multimedia Information Processing - Volume 9916Many studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel discriminative classifier fusion method, which ...
Comments
Information & Contributors
Information
Published In
December 2024
433 pages
EISSN:1556-4711
DOI:10.1145/3613626
- Editor:
- Karina Rodriguez Echavarria,
- Guest Editorss:
- Enrico Daga,
- Rossana Damiano,
- Lily Díaz-Kommonen,
- Tsvi Kuflik,
- Manuel Striani
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 December 2024
Online AM: 07 August 2024
Accepted: 04 June 2024
Revised: 30 April 2024
Received: 26 May 2023
Published in JOCCH Volume 17, Issue 4
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- CSC and Loughborough University
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 209Total Downloads
- Downloads (Last 12 months)209
- Downloads (Last 6 weeks)25
Reflects downloads up to 03 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in