摘要本文旨在通过层级多标签文本分类方法，从论文内容的角度开展跨学科测度研究，以更直观地揭示学科间的交叉融合特征，从而深化和完善跨学科知识体系。针对现有学术论文层级多标签学科分类中对论文文本、学科标签语义及其层级结构，以及论文文本与学科标签交互关系等多维特征建模不充分的问题，本文构建了一种多重特征协同的层级多标签学科分类模型（hierarchical multi-label discipline classification based on multiple feature，HMDCMF），利用ECOOM（Centre for Research and Development Monitoring）学科分类体系对学术论文进行学科分类，并依据学科分类概率矩阵测度不同学科的跨学科性。研究发现，与当前主流的层级多标签文本分类模型相比，HMDCMF模型效果最佳；不同学科间的交叉现象普遍存在，许多学科都在突破传统发展模式，不断融合新领域、新技术，探索新方向；HMDCMF模型能较好地解决学科标签语义匮乏、论文文本和学科标签特征关联不充分等问题，实现基于论文内容角度的跨学科测度。在理论层面，拓展了当前跨学科测度的方法体系；在实践层面，可促进学科间的知识整合与协同创新，为深化跨学科知识体系建构提供量化分析依据。
1 陈其荣. 诺贝尔自然科学奖与跨学科研究[J]. 上海大学学报(社会科学版), 2009, 16(5): 48-62. 2 李江. “跨学科性”的概念框架与测度[J]. 图书情报知识, 2014(3): 87-93. 3 刘清民, 王芳. 探索信息资源管理的跨学科性: 基于多标签分类的分析[J]. 情报学报, 2025, 44(1): 75-92. 4 吕琦, 上官燕红, 张琳, 等. 基于文本内容自动分类的跨学科测度研究[J]. 数据分析与知识发现, 2023, 7(4): 56-67. 5 吕琦, 上官燕红, 李锐. 基于参考文献和文本内容学科分类的跨学科测度研究[J]. 情报学报, 2024, 43(8): 976-991. 6 Yu C, Shen Y, Mao Y. Constrained sequence-to-tree generation for hierarchical text classification[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2022: 1865-1869. 7 Wang L X, Chen Z L, Lin N K, et al. An interdisciplinary literature classifier based on multi-task multi-label learning[C]// Proceedings of the 2021 International Conference on Asian Language Processing. Piscataway: IEEE, 2021: 183-188. 8 赵海燕, 曹杰, 陈庆奎, 等. 层次多标签文本分类方法[J]. 小型微型计算机系统, 2022, 43(4): 673-683. 9 张智雄, 赵旸, 刘欢. 构建面向实际应用的科技文献自动分类引擎[J]. 中国图书馆学报, 2022, 48(4): 104-115. 10 Gialitsis N, Kotitsas S, Papageorgiou H. SciNoBo: a hierarchical multi-label classifier of scientific publications[C]// Proceedings of the Companion Proceedings of the Web Conference 2022. New York: ACM Press, 2022: 800-809. 11 Chen H B, Ma Q L, Lin Z X, et al. Hierarchy-aware label semantics matching network for hierarchical text classification[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2021: 4370-4379. 12 Lu J Y, Zhang H, Shen Z X, et al. Multi-task hierarchical cross-attention network for multi-label text classification[C]// Proceedings of the 11th CCF International Conference on Natural Language Processing and Chinese Computing. Cham: Springer, 2022: 156-167. 13 徐江玲, 陈兴荣. 基于层级图标签表示网络的多标签文本分类[J]. 计算机应用研究, 2024, 41(2): 388-392, 407. 14 陈帅朴, 钱宇星, 钱志强, 等. 多重特征关联和图注意力网络融合的文献分类方法研究——以中文医学文献为例[J]. 情报学报, 2024, 43(4): 470-490. 15 戎璐. 基于图双注意力网络的图书类目分类方法研究[J]. 图书馆学研究, 2023(5): 47-60. 16 Ma Y L, Liu X F, Zhao L J, et al. Hybrid embedding-based text representation for hierarchical multi-label text classification[J]. Expert Systems with Applications, 2022, 187: 115905. 17 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. Stroudsburg: Association for Computational Linguistics, 2020: 1106-1117. 18 王岳, 李雅文, 李昂. 科技资源文本层次多标签分类方法[J]. 计算机工程与应用, 2023, 59(13): 92-98. 19 黄颖, 张琳, 孙蓓蓓, 等. 跨学科的三维测度——外部知识融合、内在知识会聚与科学合作模式[J]. 科学学研究, 2019, 37(1): 25-35. 20 Zhang W Y, Shi S S, Huang X L, et al. The distinctiveness of author interdisciplinarity: a long-neglected issue in research on interdisciplinarity[J]. Journal of Information Science, 2022, 48(1): 90-105. 21 Pham H S, Vancraeynest B, Poelmans H, et al. Identifying interdisciplinary research in research projects[J]. Scientometrics, 2023, 128(10): 5521-5544. 22 Zhang L, Sun B B, Chinchilla-Rodríguez Z, et al. Interdisciplinarity and collaboration: on the relationship between disciplinary diversity in departmental affiliations and reference lists[J]. Scientometrics, 2018, 117(1): 271-291. 23 Abramo G, D’Angelo C A, Zhang L. A comparison of two approaches for measuring interdisciplinary research output: the disciplinary diversity of authors vs the disciplinary diversity of the reference list[J]. Journal of Informetrics, 2018, 12(4): 1182-1193. 24 Hou J H, Li H, Zhang Y. Influence of interdisciplinarity of scientific papers on the durability of citation diffusion: a perspective from citation discontinuance[J]. Journal of Informetrics, 2024, 18(3): 101525. 25 杨杰, 王左戎, 邓三鸿, 等. 基于参考文献的论文跨学科性、跨时域性及其影响力研究[J]. 情报学报, 2024, 43(9): 1003-1014. 26 Ke Q. Interdisciplinary research and technological impact: evidence from biomedicine[J]. Scientometrics, 2023, 128(4): 2035-2077. 27 李长玲, 王浩, 高峰, 等. 以引用句为桥的知识跨学科输出影响力分析——以信息资源管理学科为例[J]. 图书情报工作, 2025, 69(5): 50-58. 28 Rafols I, Meyer M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience[J]. Scientometrics, 2010, 82(2): 263-287. 29 Leydesdorff L, Wagner C S, Bornmann L. Interdisciplinarity as diversity in citation patterns among journals: Rao-Stirling diversity, relative variety, and the Gini coefficient[J]. Journal of Informetrics, 2019, 13(1): 255-269. 30 章成志, 吴小兰. 跨学科研究综述[J]. 情报学报, 2017, 36(5): 523-535. 31 Kim H, Park H, Song M. Developing a topic-driven method for interdisciplinarity analysis[J]. Journal of Informetrics, 2022, 16(2): 101255. 32 徐庶睿, 卢超, 章成志. 术语引用视角下的学科交叉测度——以PLOS ONE上六个学科为例[J]. 情报学报, 2017, 36(8): 809-820. 33 王卫军, 宁致远, 杜一, 等. 基于多标签分类的科技文献学科交叉研究性质识别[J]. 数据分析与知识发现, 2023, 7(1): 102-112. 34 刘江峰, 张冉, 王东波, 等. 追溯科学融合的历史轨迹: AIGC赋能交叉科学测度研究[J]. 图书情报工作, 2024, 68(7): 137-150. 35 Gl?nzel W, Schubert A. A new classification scheme of science fields and subfields designed for scientometric evaluation purposes[J]. Scientometrics, 2003, 56(3): 357-367. 36 Beltagy I, Cohan A, Lo K. SciBERT: pretrained contextualized embeddings for scientific text[PP/OL]. V3. arxiv (2019-09-10). https://arxiv.org/pdf/1903.10676. 37 章成志, 张颖怡. 基于学术论文全文的研究方法实体自动识别研究[J]. 情报学报, 2020, 39(6): 589-600. 38 Salton G, Buckley C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5): 513-523. 39 Stirling A. A general framework for analysing diversity in science, technology and society[J]. Journal of the Royal Society Interface, 2007, 4(15): 707-719. 40 Zhang L, Rousseau R, Gl?nzel W. Diversity of references as an indicator of the interdisciplinarity of journals: taking similarity between subject fields into account[J]. Journal of the Association for Information Science & Technology, 2016, 67(5): 1257-1265. 41 Hamers L, Hemeryck Y, Herweyers G, et al. Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula[J]. Information Processing & Management, 1989, 25(3): 315-318. 42 Brillouin L, Hellwarth R W. Science and information theory[J]. Physics Today, 1956, 9(12): 39-40. 43 Kowsari K, Brown D E, Heidarysafa M, et al. HDLTex: hierarchical deep learning for text classification[C]// Proceedings of the 16th IEEE International Conference on Machine Learning and Applications. Piscataway: IEEE, 2017: 364-371. 44 Yang P C, Sun X, Li W, et al. SGM: sequence generation model for multi-label classification[C]// Proceedings of the 27th International Conference on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 3915-3926. 45 Kim Y. Convolutional neural networks for sentence classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1746-1751. 46 Liu P F, Qiu X P, Huang X J. Recurrent neural network for text classification with multi-task learning[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 2873-2879. 47 Shen T, Zhou T Y, Long G D, et al. Bi-directional block self-attention for fast and memory-efficient sequence modeling[C]// Proceedings of the 6th International Conference on Learning Representations. Appleton: ICLR, 2018. DOI: 10.48550/arXiv.1804.00857. 48 Deng Z F, Peng H, He D X, et al. HTCInfoMax: a global model for hierarchical text classification via information maximization[C]// Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2021: 3259-3265. 49 Wang Z H, Wang P Y, Huang L Z, et al. Incorporating hierarchy into text encoder: a contrastive learning approach for hierarchical text classification[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2022: 7109-7119. 50 曹渝昆, 魏子越, 唐艺嘉, 等. 深层次标签辅助分类任务的层次标签文本分类方法[J]. 计算机工程与应用, 2024, 60(10): 105-112. 51 Porter A L, Cohen A S, Roessner J D, et al. Measuring researcher interdisciplinarity[J]. Scientometrics, 2007, 72(1): 117-147.
