This work is licensed under the Creative Commons Attribution 4.0 International License.
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/s11192-016-2071-6BartolT.BudimirG.JuznicP.StoparK. (2016). Mapping and classification of agriculture in Web of Science: Other subject categories and research fields may benefit. , 109(7), 979–996. https://doi.org/10.1007/s11192-016-2071-6Search in Google Scholar
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.2022.05.017CareyN.HarteM.CullaghM. L. (2022). A text-mining tool generated title-abstract screening workload savings: Performance evaluation versus single-human screening. , 149(9), 53–59. https://doi.org/10.1016/j.jclinepi.2022.05.017Search in Google Scholar
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/MIPRO57284.2023.10159911ČeovićH.ŠilićM.DelačG.VladimirK. (2023). An overview of diffusion models for text generation. , 941–946. https://doi.org/10.23919/MIPRO57284.2023.10159911Search in Google Scholar
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.2022.3183685ChangC.TangY.LongY. X.HuK.LiY.LiJ. G.WangC. D. (2023). Multi-information preprocessing event extraction with BiLSTM-CRF attention for academic knowledge graph construction. , 10(5), 2713–2724. https://doi.org/10.1109/TCSS.2022.3183685Search in Google Scholar
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.1000-0135.2021.01.005ChengQ. K.LiP. C.ZhangG. B.LuW. (2021). Recognition of lexical functions in academic texts: Problem method extraction based on title generation strategy and attention mechanism. , 40(1), 43–52. https://doi.org/10.3772/j.issn.1000-0135.2021.01.005Search in Google Scholar
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.2017.11.001ChuH.KeQ. (2017). Research methods: What’s in the name?. , 39(4), 284–294. https://doi.org/10.1016/j.lisr.2017.11.001Search in Google Scholar
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-xDongK.XuH.LuoR.WeiL.FangS. (2018). An integrated method for interdisciplinary topic identification and prediction: A case study on information science and library science. , 115(2), 849–868. https://doi.org/10.1007/s11192-018-2694-xSearch in Google Scholar
Du, T. (2020). A study on the classification of the first level subjects in SCI papers. [Master thesis, Shanxi University]. Wanfang Dissertations & Theses.DuT. (2020). . [Master thesis, Shanxi University]. Wanfang Dissertations & Theses.Search in Google Scholar
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.26DuZ.QianY.LiuX.DingM.QiuJ.YangZ.TangJ. (2022). GLM: General language model pretraining with autoregressive blank infilling. , 320–335. https://doi.org/10.18653/v1/2022.acl-long.26Search in Google Scholar
Durgun, B. (2017). Multidisciplinary, interdisciplinary and transdisciplinary approaches to the scientific study. Manisa CBU Journal of Institute of Health Science, 4 (Supplement), 676.DurgunB. (2017). Multidisciplinary, interdisciplinary and transdisciplinary approaches to the scientific study. , 4 (Supplement), 676.Search in Google Scholar
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/1000131503FärberM.AlbersA.SchüberF. (2021). Identifying used methods and datasets in scientific publications. , 1–9. https://doi.org/10.5445/IR/1000131503Search in Google Scholar
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/S18-1111GaborK.BuscaldiD.SchumannA. K.QasemiZadehB.ZargayounaH.CharnoisT. (2018). SemEval-2018 task 7: Semantic relation extraction and classification in scientific papers. , 679–688. https://doi.org/10.18653/v1/S18-1111Search in Google Scholar
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/s11042-023-15224-0GoyalR.KumarP.SinghV. P. (2023). A systematic survey on automated text generation tools and techniques: Application, evaluation, and challenges. , 82(28), 43089–43144. https://doi.org/10.1007/s11042-023-15224-0Search in Google Scholar
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.GuptaS.ManningC. D. (2011). Analyzing the dynamics of research by extracting key aspects of scientific papers. , 1–9.Search in Google Scholar
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.2022.908733HeT.FuW.XuJ.ZhangZ.ZhouJ.YinY.XieZ. (2022). Discovering interdisciplinary research based on neural networks. , 10(Article 908733), 1–8. https://doi.org/10.3389/fbioe.2022.908733Search in Google Scholar
Heffernan, K., & Teufel, S. (2018). Identifying problems and solutions in scientific text. Scientometrics, 116(2), 1367–1382. https://doi.org/10.1007/s11192-018-2718-6HeffernanK.TeufelS. (2018). Identifying problems and solutions in scientific text. , 116(2), 1367–1382. https://doi.org/10.1007/s11192-018-2718-6Search in Google Scholar
Houncbo, H., & Mercer, R. E. (2012). Method mention extraction from scientific research papers. Proceedings of COLING 2012, Mumbai, India, 1211–1222.HouncboH.MercerR. E. (2012). Method mention extraction from scientific research papers. , 1211–1222.Search in Google Scholar
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.23538HowisonJ.BullardJ. (2015). Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature. , 67(9), 2137–2155. https://doi.org/10.1002/asi.23538Search in Google Scholar
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-zHuangX. M.ZhuP. H.ChenY. W.MaJ. (2023). A transfer learning approach to interdisciplinary document classification with keyword-based explanation. , 128(12), 6449–6469. https://doi.org/10.1007/sH192-023-04825-zSearch in Google Scholar
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/s11192-021-04060-4JesenkoB.SchlöglC. (2021). The effect of web of science subject categories on clustering: The case of data-driven methods in business and economic sciences. , 126(2), 6785–6801. https://doi.org/10.1007/s11192-021-04060-4Search in Google Scholar
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_20LeeH. C.MaoJ. C. (2004). Information extraction by embedding HMM to the set of induced linguistic features. In ApostolicoA.MelucciM. (Eds.), : Vol. 3246. (pp. 134–135), Springer. https://doi.org/10.1007/978-3-540-30213-1_20Search in Google Scholar
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.703LewisM.LiuY.GoyalN.GhazvininejadM.MohamedA.LevyO.StoyanovV.ZettlemoyerL. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. , 7871–7880. https://doi.org/10.18653/v1/2020.acl-main.703Search in Google Scholar
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.2300410LiB.YangP.SunY. K.HuZ. J.YiM. (2024). Advances and challenges in artificial intelligence text generation. , 25(1), 64–83. https://doi.org/10.1631/FITEE.2300410Search in Google Scholar
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.LiC.YuH. (2018). Multidisciplinary research cooperation in higher education research institutions: A bibliometric analysis based on four institutions’ data. , 2018(4), 75–79.Search in Google Scholar
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.0252-3116.2023.09.014LiX. S.ZhangZ. X.LiuY.WangY. F. (2023). A study on the method of identifying research question sentences in scientific articles. , 67(9), 132–140. https://doi.org/10.13266/j.issn.0252-3116.2023.09.014Search in Google Scholar
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/D18-1360LuanY.HeL.OstendorfM.HajishirziH. (2018). Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. , 3219–3232. https://doi.org/10.18653/v1/D18-1360Search in Google Scholar
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/D17-1279LuanY.OstendorfM.HajishirziH. (2017). Scientific information extraction with semisupervised neural tagging. , 2641–2651. https://doi.org/10.18653/v1/D17-1279Search in Google Scholar
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_00014MilojevićS. (2020). Practical method to reclassify Web of Science articles into unique subject categories and broad disciplines. , 1(1), 183–206. https://doi.org/10.1162/qss_a_00014Search in Google Scholar
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/10.17226/11153National Academy of Sciences, National Academy of Engineering, & Institute of Medicine of the National Academies. (2005). . The National Academies Press. https://doi.org/10.17226/11153Search in Google Scholar
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/10.3115/1073083.1073135PapineniK.RoukosS.WardT.ZhuW. (2002). Bleu: A method for automatic evaluation of machine translation. , 311–318. https://doi.org/10.3115/1073083.1073135Search in Google Scholar
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.2017.11.3.3PutraJ. W. G.KhodraM. L. (2017). Automatic title generation in scientific articles for authorship assistance: A summarization approach. , 11(3), 253–267. https://doi.org/10.5614/itbj.ict.res.appl.2017.11.3.3Search in Google Scholar
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.1000-7490.2020.10.028RanY.HanH.ZhangY.WengM.GaoX.PengK. (2020). Large scale text hierarchical classification method based on stacking ensemble learning. , 43(10), 171–176,182. https://doi.org/10.16353/j.cnki.1000-7490.2020.10.028Search in Google Scholar
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/N18-2028SongY.ShiS.LiJ.ZhangH. (2018). Directional Skip-Gram: Explicitly distinguishing left and right context for word embeddings. , 2018(2), 175–180. https://doi.org/10.18653/v1/N18-2028Search in Google Scholar
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.2021015SuoC. J.LaiH. M. (2021). Types and Description Rules of Problem Knowledge Units in Academic Papers. , 47(2), 95–109. https://doi.org/10.13530/j.cnki.jlis.2021015Search in Google Scholar
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.TateisiY.ShidaharaY.MiyaoY.AizawaA. (2013). Ralation annotation for understanding research papers. , 140–148.Search in Google Scholar
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.2016.2546302TuarobS.HatiaS.MitraP.GilesC. L. (2016). Algorithmseer: A system for extracting and searching for algorithms in scholarly big data. , 2(1), 3–17. https://doi.org/10.1109/TBDATA.2016.2546302Search in Google Scholar
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.WangH.HuangW.WangJ. (2015). On the status of distant interdisciplinary academic cooperation in Sino-US research universities from the perspective of collaborative innovation. , 2015(4), 49–54.Search in Google Scholar
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/s11192-023-04776-5WangZ. Y.ChenJ.ChenJ. P.ChenH. (2023). Identifying interdisciplinary topics and their evolution based on BERTopic. . https://doi.org/10.1007/s11192-023-04776-5Search in Google Scholar
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.1000-7490.2023.01.022YiH. F.LiuX. W.LongY. X. (2023). Research on mining domain key technical problems based on multi-text analysis. , 46(1), 187–196. https://doi.org/10.16353/j.cnki.1000-7490.2023.01.022Search in Google Scholar
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.ZengA.LiuX.DuZ.WangZ.LaiH.DingM.TangJ. (2022). Glm-130b: An open bilingual pre-trained model. .Search in Google Scholar
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-03-2022-0130ZengJ. X.CaoS. J.ChenY. J.PanP.CaiY. F. (2023). Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements. , 75(3), 589–617. https://doi.org/10.1108/AJIM-03-2022-0130Search in Google Scholar