References
- Bartol, T., Budimir, G., Juznic, P., & Stopar, K. (2016). Mapping and classification of agriculture in Web of Science: Other subject categories and research fields may benefit. Scientometrics, 109(7), 979–996. https://doi.org/10.1007/s11192-016-2071-6
- Carey, N., Harte, M., & Cullagh, M. L. (2022). A text-mining tool generated title-abstract screening workload savings: Performance evaluation versus single-human screening. Journal of Clinical Epidemiology, 149(9), 53–59. https://doi.org/10.1016/j.jclinepi.2022.05.017
- Čeović, H., Šilić, M., Delač, G., & Vladimir, K. (2023). An overview of diffusion models for text generation. Proceeding of the 46th MIPRO ICT and Electronics Convention (MIPRO), 941–946. https://doi.org/10.23919/MIPRO57284.2023.10159911
- Chang, C., Tang, Y., Long, Y. X., Hu, K., Li, Y., Li, J. G., & Wang, C. D. (2023). Multi-information preprocessing event extraction with BiLSTM-CRF attention for academic knowledge graph construction. IEEE Transactions on Computational Social Systems, 10(5), 2713–2724. https://doi.org/10.1109/TCSS.2022.3183685
- Cheng, Q. K., Li, P. C., Zhang, G. B., & Lu, W. (2021). Recognition of lexical functions in academic texts: Problem method extraction based on title generation strategy and attention mechanism. Journal of the China Society for Science and Technical Information, 40(1), 43–52. https://doi.org/10.3772/j.issn.1000-0135.2021.01.005
- Chu, H., & Ke, Q. (2017). Research methods: What’s in the name?. Library & Information Science Research, 39(4), 284–294. https://doi.org/10.1016/j.lisr.2017.11.001
- Dong, K., Xu, H., Luo, R., Wei, L., & Fang, S. (2018). An integrated method for interdisciplinary topic identification and prediction: A case study on information science and library science. Scientometrics, 115(2), 849–868. https://doi.org/10.1007/s11192-018-2694-x
- Du, T. (2020). A study on the classification of the first level subjects in SCI papers. [Master thesis, Shanxi University]. Wanfang Dissertations & Theses.
- Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., & Tang, J. (2022). GLM: General language model pretraining with autoregressive blank infilling. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, Volume 1: Long Papers, 320–335. https://doi.org/10.18653/v1/2022.acl-long.26
- Durgun, B. (2017). Multidisciplinary, interdisciplinary and transdisciplinary approaches to the scientific study. Manisa CBU Journal of Institute of Health Science, 4 (Supplement), 676.
- Färber, M., Albers, A., & Schüber, F. (2021). Identifying used methods and datasets in scientific publications. Proceedings of the Workshop on Scientific Document Understanding: Co-located with 35th AAAI Conference on Artificial Inteligence (AAAI 2021), Remote, 1–9. https://doi.org/10.5445/IR/1000131503
- Gabor, K., Buscaldi, D., Schumann, A. K., QasemiZadeh, B., Zargayouna, H., & Charnois, T. (2018). SemEval-2018 task 7: Semantic relation extraction and classification in scientific papers. Proceedings of the 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, United States, 679–688. https://doi.org/10.18653/v1/S18-1111
- Goyal, R., Kumar, P., & Singh, V. P. (2023). A systematic survey on automated text generation tools and techniques: Application, evaluation, and challenges. Multimedia Tools and Applications, 82(28), 43089–43144. https://doi.org/10.1007/s11042-023-15224-0
- Gupta, S., & Manning, C. D. (2011). Analyzing the dynamics of research by extracting key aspects of scientific papers. Proceedings of 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, 1–9.
- He, T., Fu, W., Xu, J., Zhang, Z., Zhou, J., Yin, Y., & Xie, Z. (2022). Discovering interdisciplinary research based on neural networks. Frontiers in Bioengineering and Biotechnology, 10(Article 908733), 1–8. https://doi.org/10.3389/fbioe.2022.908733
- Heffernan, K., & Teufel, S. (2018). Identifying problems and solutions in scientific text. Scientometrics, 116(2), 1367–1382. https://doi.org/10.1007/s11192-018-2718-6
- Houncbo, H., & Mercer, R. E. (2012). Method mention extraction from scientific research papers. Proceedings of COLING 2012, Mumbai, India, 1211–1222.
- Howison, J., & Bullard, J. (2015). Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature. Journal of the Association for Information Science and Technology, 67(9), 2137–2155. https://doi.org/10.1002/asi.23538
- Huang, X. M., Zhu, P. H., Chen, Y. W., & Ma, J. (2023). A transfer learning approach to interdisciplinary document classification with keyword-based explanation. Scientometrics, 128(12), 6449–6469. https://doi.org/10.1007/s11192-023-04825-z
- Jesenko, B., & Schlögl, C. (2021). The effect of web of science subject categories on clustering: The case of data-driven methods in business and economic sciences. Scientometrics, 126(2), 6785–6801. https://doi.org/10.1007/s11192-021-04060-4
- Lee, H. C., & Mao, J. C. (2004). Information extraction by embedding HMM to the set of induced linguistic features. In Apostolico, A. & Melucci, M. (Eds.), Lecture Notes in Computer Science: Vol. 3246. (pp. 134–135), Springer. https://doi.org/10.1007/978-3-540-30213-1_20
- Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 7871–7880. https://doi.org/10.18653/v1/2020.acl-main.703
- Li, B., Yang, P., Sun, Y. K., Hu, Z. J., & Yi, M. (2024). Advances and challenges in artificial intelligence text generation. Frontiers of Information Technology & Electronic Engineering, 25(1), 64–83. https://doi.org/10.1631/FITEE.2300410
- Li, C., & Yu, H. (2018). Multidisciplinary research cooperation in higher education research institutions: A bibliometric analysis based on four institutions’ data. Shanghai Journal of Educational Evaluation, 2018(4), 75–79.
- Li, X. S., Zhang, Z. X., Liu, Y., & Wang, Y. F. (2023). A study on the method of identifying research question sentences in scientific articles. Library and Information Service, 67(9), 132–140. https://doi.org/10.13266/j.issn.0252-3116.2023.09.014
- Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018). Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 3219–3232. https://doi.org/10.18653/v1/D18-1360
- Luan, Y., Ostendorf, M., & Hajishirzi, H. (2017). Scientific information extraction with semisupervised neural tagging. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2641–2651. https://doi.org/10.18653/v1/D17-1279
- Milojević, S. (2020). Practical method to reclassify Web of Science articles into unique subject categories and broad disciplines. Quantitative Science Studies, 1(1), 183–206. https://doi.org/10.1162/qss_a_00014
- National Academy of Sciences, National Academy of Engineering, & Institute of Medicine of the National Academies. (2005). Facilitating Multidisciplinary Research. The National Academies Press. https://doi.org/10.17226/11153
- Papineni, K., Roukos, S., Ward, T., & Zhu, W. (2002). Bleu: A method for automatic evaluation of machine translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 311–318. https://doi.org/10.3115/1073083.1073135
- Putra, J. W. G., & Khodra, M. L. (2017). Automatic title generation in scientific articles for authorship assistance: A summarization approach. Journal of ICT Research and Applications, 11(3), 253–267. https://doi.org/10.5614/itbj.ict.res.appl.2017.11.3.3
- Ran, Y., Han, H., Zhang, Y., Weng, M., Gao, X., & Peng, K. (2020). Large scale text hierarchical classification method based on stacking ensemble learning. Information Studies: Theory & Application, 43(10), 171–176,182. https://doi.org/10.16353/j.cnki.1000-7490.2020.10.028
- Song, Y., Shi, S., Li, J., & Zhang, H. (2018). Directional Skip-Gram: Explicitly distinguishing left and right context for word embeddings. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, Louisiana, 2018(2), 175–180. https://doi.org/10.18653/v1/N18-2028
- Suo, C. J., & Lai, H. M. (2021). Types and Description Rules of Problem Knowledge Units in Academic Papers. Journal of Libary Science in China, 47(2), 95–109. https://doi.org/10.13530/j.cnki.jlis.2021015
- Tateisi, Y., Shidahara, Y., Miyao, Y., & Aizawa, A. (2013). Ralation annotation for understanding research papers. Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, Sofia, Bulgaria, 140–148.
- Tuarob, S., Hatia, S., Mitra, P., & Giles, C. L. (2016). Algorithmseer: A system for extracting and searching for algorithms in scholarly big data. IEEE Transactions on Big Data, 2(1), 3–17. https://doi.org/10.1109/TBDATA.2016.2546302
- Wang, H., Huang, W., & Wang, J. (2015). On the status of distant interdisciplinary academic cooperation in Sino-US research universities from the perspective of collaborative innovation. Research in Higher Education of Engineering, 2015(4), 49–54.
- Wang, Z. Y., Chen, J., Chen, J. P., & Chen, H. (2023). Identifying interdisciplinary topics and their evolution based on BERTopic. Scientometrics. https://doi.org/10.1007/s11192-023-04776-5
- Yi, H. F., Liu, X. W., & Long, Y. X. (2023). Research on mining domain key technical problems based on multi-text analysis. Information Studies: Theory & Application, 46(1), 187–196. https://doi.org/10.16353/j.cnki.1000-7490.2023.01.022
- Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., … & Tang, J. (2022). Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414.
- Zeng, J. X., Cao, S. J., Chen, Y. J., Pan, P., & Cai, Y. F. (2023). Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements. ASLIB Journal of Information Management, 75(3), 589–617. https://doi.org/10.1108/AJIM-03-2022-0130