|
|
Hierarchical Multi-label Classification of Children's Literature for Graded Reading |
Cheng Quan(),Dong Jia |
School of Economics and Management, Fuzhou University, Fuzhou 350116, China |
|
|
Abstract [Objective] This study constructs a hierarchical multi-label classification model for children's literature, aiming to realize the automatic classification of children's books, guiding young readers to select books suitable for their development needs. [Methods] We materialized the concept of graded reading into a hierarchical classification label system for children's literature. Then, we built ERNIE-HAM model using deep learning techniques and applied it to the hierarchical multi-label text classification system. [Results] Compared with the four pre-training models, the ERNIE-HAM model performed well in the second and third hierarchical classification levels for children's books. Compared to the single-level algorithm, the hierarchical algorithm improved the values for the second and third levels by about 11%. Compared to the two hierarchical multi-label classification models, HFT-CNN and HMCN, the ERNIE-HAM model improved the third level by 12.79% and 6.48% in the classification results, respectively. [Limitations] The overall classification performance of the proposed model can be further improved, and future work should focus on expanding the dataset and refining the algorithm design. [Conclusions] The ERNIE-HAM model is effective in the hierarchical multi-label classification for children's literature.
|
Received: 23 June 2022
Published: 07 September 2023
|
|
Fund:National Social Science Fund of China(19BTQ072) |
Corresponding Authors:
Cheng Quan,ORCID:0000-0002-7302-4527,E-mail: chengquan@fzu.edu.cn。
|
[1] |
中国新闻出版研究院全国国民阅读调查课题组. 第十八次全国国民阅读调查主要发现[J]. 出版发行研究, 2021(4): 19-24.
|
[1] |
(The Working Group of National Reading Survey of Chinese Academy of Press & Publications. The Main Findings of the 18th National Reading Survey[J]. Publishing Research, 2021(4): 19-24.)
|
[2] |
周力虹, 刘芳. 图书馆未成年人数字分级阅读服务研究[J]. 图书馆建设, 2014(12): 59-62.
|
[2] |
(Zhou Lihong, Liu Fang. Research on the Digital Grade Reading Service Oriented to Minors in the Library[J]. Library Development, 2014(12): 59-62.)
|
[3] |
马小翠, 卜璐. 少儿图书分级阅读的理论与实践研究[J]. 图书馆研究与工作, 2020(9): 50-53, 63.
|
[3] |
(Ma Xiaocui, Bu Lu. Research on the Theory and Practice of Children's Book Graded Reading[J]. Library Science Research & Work, 2020(9): 50-53, 63.)
|
[4] |
McGeown S P, Osborne C, Warhurst A, et al. Understanding Children's Reading Activities: Reading Motivation, Skill and Child Characteristics as Predictors[J]. Journal of Research in Reading, 2016, 39(1): 109-125.
doi: 10.1111/jrir.v39.1
|
[5] |
黄宁. 浅析图书分级对儿童阅读的影响[J]. 图书馆工作与研究, 2015(3): 102-104.
|
[5] |
(Huang Ning. The Influence of Book Classification to the Children's Reading[J]. Library Work and Study, 2015(3): 102-104.)
|
[6] |
张小琴, 李孝滢, 王昊. 南京地区儿童阅读现状及分级阅读需求调查研究[J]. 图书馆理论与实践, 2019(8): 74-78.
|
[6] |
(Zhang Xiaoqin, Li Xiaoying, Wang Hao. A Survey of Children's Reading Status and Graded Reading Needs in Nanjing[J]. Library Theory and Practice, 2019(8): 74-78.)
|
[7] |
王昊, 严明, 苏新宁. 基于机器学习的中文书目自动分类研究[J]. 中国图书馆学报, 2010, 36(6): 28-39.
|
[7] |
(Wang Hao, Yan Ming, Su Xinning. Research on Automatic Classification for Chinese Bibliography Based on Machine Learning[J]. Journal of Library Science in China, 2010, 36(6): 28-39.)
|
[8] |
邹鼎杰. 基于知识图谱和贝叶斯分类器的图书分类[J]. 计算机工程与设计, 2020, 41(6): 1796-1801.
|
[8] |
(Zou Dingjie. Book Classification Based on Knowledge Graph and Bayesian Classifier[J]. Computer Engineering and Design, 2020, 41(6): 1796-1801.)
|
[9] |
潘辉. 基于极限学习机的自动化图书信息分类技术[J]. 现代电子技术, 2019, 42(17): 183-186.
|
[9] |
(Pan Hui. Automated Book Information Classification Technology Based on Extreme Learning Machine[J]. Modern Electronics Technique, 2019, 42(17): 183-186.)
|
[10] |
潘峻. 基于双向LSTM的图书分类系统的设计与实现[J]. 信息技术, 2020, 44(1): 67-70, 74.
|
[10] |
(Pan Jun. Development of Book Classification System Based on Bi-LSTM[J]. Information Technology, 2020, 44(1): 67-70, 74.)
|
[11] |
邓三鸿, 傅余洋子, 王昊. 基于LSTM模型的中文图书多标签分类研究[J]. 数据分析与知识发现, 2017, 1(7): 52-60.
|
[11] |
(Deng Sanhong, Fu Yuyangzi, Wang Hao. Multi-Label Classification of Chinese Books with LSTM Model[J]. Data Analysis and Knowledge Discovery, 2017, 1(7): 52-60.)
|
[12] |
蒋彦廷, 胡韧奋. 基于BERT模型的图书表示学习与多标签分类研究[J]. 新世纪图书馆, 2020(9): 38-44.
|
[12] |
(Jiang Yanting, Hu Renfen. Representation Learning and Multi-Label Classification of Books Based on BERT[J]. New Century Library, 2020(9): 38-44.)
|
[13] |
Huang W, Chen E H, Liu Q, et al. Hierarchical Multi-Label Text Classification: An Attention-Based Recurrent Network Approach[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1051-1060.
|
[14] |
Gong J B, Teng Z Y, Teng Q, et al. Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification[J]. IEEE Access, 2020(8): 30885-30896.
|
[15] |
Sinha K, Dong Y, Cheung J C K, et al. A Hierarchical Neural Attention-Based Text Classifier[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 817-823.
|
[16] |
Banerjee S, Akkaya C, Perez-Sorrosal F, et al. Hierarchical Transfer Learning for Multi-Label Text Classification[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 6295-6300.
|
[17] |
Peng H, Li J X, Wang S Z, et al. Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNS for Large-Scale Multi-Label Text Classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(6): 2505-2519.
doi: 10.1109/TKDE.2019.2959991
|
[18] |
Mao Y N, Tian J J, Han J W, et al. Hierarchical Text Classification with Reinforced Label Assignment[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 445-455.
|
[19] |
Wu J W, Xiong W H, Wang W Y. Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 4353-4363.
|
[20] |
Zhou J, Ma C P, Long D K, et al. Hierarchy-Aware Global Model for Hierarchical Text Classification[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 1106-1117.
|
[21] |
皮亚杰. 发生认识论原理[M]. 王宪钿,译. 北京: 商务印书馆, 1981:132-134.
|
[21] |
(Piaget J. Principles of Genetic Epistemology[M]. Translate by Wang Xiantian. Beijing: The Commercial Press, 1981:132-134.)
|
[22] |
中华人民共和国教育部.3-6 岁儿童学习与发展指南[EB/OL]. [2012-10-09]. http://www.moe.gov.cn/jyb_xwfb/xw_zt/moe_357/jyzt_2015nztzl/xueqianjiaoyu/yaowen/202104/W020210820338905908083.pdf.
|
[22] |
(Ministry of Education of the People's Republic of China. Early Learning and Development Guideline[EB/OL]. [2012-10-09]. http://www.moe.gov.cn/jyb_xwfb/xw_zt/moe_357/jyzt_2015nztzl/xueqianjiaoyu/yaowen/202104/W020210820338905908083.pdf.)
|
[23] |
Sun Y, Wang S H, Li Y K, et al. ERNIE: Enhanced Representation Through Knowledge Integration[OL]. arXiv Preprint, arXiv: 1904.09223.
|
[24] |
Vaswani A, Shazeer N, Parmar N, et al. Attention is all You Need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.
|
[25] |
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1 (Long and Short Papers). 2019: 4171-4186.
|
[26] |
Paszke A, Gross S, Chintala S, et al. Automatic Differentiation in PyTorch[C]// Proceedings of the 31st Conference on Neural Information Processing Systems. 2017.
|
[27] |
He K M, Zhang X Y, Ren S Q, et al. Deep Residual Learning for Image Recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
|
[28] |
Loshchilov I, Hutter F. Decoupled Weight Decay Regularization[OL]. arXiv Preprint, arXiv: 1711.05101.
|
[29] |
Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A Simple Way to Prevent Neural Networks from Overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
|
[30] |
Lan Z Z, Chen M D, Goodman S, et al. ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations[OL]. arXiv Preprint, arXiv: 1909.11942.
|
[31] |
Liu Y H, Ott M, Goyal N, et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach[OL]. arXiv Preprint, arXiv: 1907.11692.
|
[32] |
Shimura K, Li J Y, Fukumoto F. HFT-CNN: Learning Hierarchical Category Structure for Multi-Label Short Text Categorization[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 811-816.
|
[33] |
Wehrmann J, Cerri R, Barros R C. Hierarchical Multi-Label Classification Networks[C]// Proceedings of the 35th International Conference on Machine Learning. 2018:5075-5084.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|