{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:37:36Z","timestamp":1777635456674,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007195","name":"Universit\u00e0 degli Studi di Napoli Federico II","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007195","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Artificial intelligence (AI) has advanced rapidly, but it has limited impact on biomedical text understanding due to a lack of annotated datasets (a.k.a. few-shot learning). Multi-task learning, which uses data from multiple datasets and tasks with related syntax and semantics, has potential to address this issue. However, the effectiveness of this approach heavily relies on the quality of the available data and its transferability between tasks. In this paper, we propose a framework, built upon a state-of-the-art multi-task method (i.e. MT-DNN), that leverages different publicly available biomedical datasets to enhance relation extraction performance. Our model employs a transformer-based architecture with shared encoding layers across multiple tasks, and task-specific classification layers to generate task-specific representations. To further improve performance, we utilize a knowledge distillation technique. In our experiments, we assess the impact of incorporating biomedical datasets in a multi-task learning setting and demonstrate that it consistently outperforms state-of-the-art few-shot learning methods in cases of limited data. This results in significant improvement across most datasets and few-shot scenarios, particularly in terms of recall scores.<\/jats:p>","DOI":"10.1007\/s10462-023-10484-6","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T01:01:42Z","timestamp":1681866102000},"page":"13743-13763","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Multi-task learning for few-shot biomedical relation extraction"],"prefix":"10.1007","volume":"56","author":[{"given":"Vincenzo","family":"Moscato","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Napolano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Postiglione","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giancarlo","family":"Sperl\u00ec","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"10484_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-020-03767-y","volume":"2","author":"A Afradi","year":"2020","unstructured":"Afradi A, Ebrahimabadi A (2020) Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (gep) approaches for predicting tbm penetration rate. SN Appl Sci 2:1\u201316","journal-title":"SN Appl Sci"},{"issue":"2","key":"10484_CR2","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s41062-021-00467-3","volume":"6","author":"A Afradi","year":"2021","unstructured":"Afradi A, Ebrahimabadi A (2021) Prediction of TBM penetration rate using the imperialist competitive algorithm (ICA) and quantum fuzzy logic. Innov Infrastruct Solut 6(2):103","journal-title":"Innov Infrastruct Solut"},{"issue":"2","key":"10484_CR3","doi-asserted-by":"publisher","first-page":"75","DOI":"10.33271\/mining14.02.075","volume":"14","author":"A Afradi","year":"2020","unstructured":"Afradi A, Ebrahimabadi A, Hallajian T (2020) Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel. Mining Miner Depos 14(2):75\u201384","journal-title":"Mining Miner Depos"},{"key":"10484_CR4","first-page":"1","volume":"8","author":"A Afradi","year":"2021","unstructured":"Afradi A, Ebrahimabadi A, Hallajian T (2021) Prediction of TBM penetration rate using fuzzy logic, particle swarm optimization and harmony search algorithm. Geotech Geol Eng 8:1\u201324","journal-title":"Geotech Geol Eng"},{"key":"10484_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103382","volume":"103","author":"I Alimova","year":"2020","unstructured":"Alimova I, Tutubalina E (2020) Multiple features for clinical relation extraction: a machine learning approach. J Biomed Inform 103:103382. https:\/\/doi.org\/10.1016\/j.jbi.2020.103382","journal-title":"J Biomed Inform"},{"key":"10484_CR6","doi-asserted-by":"publisher","unstructured":"Alonso HM, Plank B (2017) When is multitask learning effective? semantic sequence prediction under varying data conditions. In: Lapata M, Blunsom P, Koller A (eds) Proceedings of the 15th conference of the european chapter of the association for computational linguistics, EACL 2017, Valencia, Spain, April 3\u20137, 2017, vol 1: Long Papers, pp 44\u201353. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/e17-1005","DOI":"10.18653\/v1\/e17-1005"},{"key":"10484_CR7","doi-asserted-by":"publisher","unstructured":"Alsentzer E, Murphy J, Boag W, Weng W-H, Jindi D, Naumann T, McDermott M (2019) Publicly available clinical BERT embeddings. In: Proceedings of the 2nd clinical natural language processing workshop, pp 72\u201378. Association for Computational Linguistics, Minneapolis, Minnesota, USA. https:\/\/doi.org\/10.18653\/v1\/W19-1909","DOI":"10.18653\/v1\/W19-1909"},{"issue":"5","key":"10484_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2041-1480-2-S5-S4","volume":"2","author":"A Ben Abacha","year":"2011","unstructured":"Ben Abacha A, Zweigenbaum P (2011) Automatic extraction of semantic relations between medical entities: a rule based approach. J Biomed Semant 2(5):1\u201311. https:\/\/doi.org\/10.1186\/2041-1480-2-S5-S4","journal-title":"J Biomed Semant"},{"key":"10484_CR9","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-1-4615-5529-2_5","volume-title":"Learning to learn","author":"R Caruana","year":"1998","unstructured":"Caruana R (1998) Multitask learning. In: Thrun S, Pratt LY (eds) Learning to learn. Springer, New York, pp 95\u2013133"},{"key":"10484_CR10","doi-asserted-by":"publisher","unstructured":"Chen M, Lan G, Du F, Lobanov VS (2020) Joint learning with pre-trained transformer on named entity recognition and relation extraction tasks for clinical analytics. In: Rumshisky A, Roberts K, Bethard S, Naumann T (eds) Proceedings of the 3rd clinical natural language processing workshop, ClinicalNLP@EMNLP 2020, Online, November 19, 2020, pp. 234\u2013242. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.clinicalnlp-1.26","DOI":"10.18653\/v1\/2020.clinicalnlp-1.26"},{"key":"10484_CR11","first-page":"1321","volume":"1","author":"FS Gharehchopogh","year":"2012","unstructured":"Gharehchopogh FS, Khalifehlou Z (2012) Study on information extraction methods from text mining and natural language processing perspectives. AWER Proc Inf Technol Comput Sci 1:1321\u20131327","journal-title":"AWER Proc Inf Technol Comput Sci"},{"issue":"5","key":"10484_CR12","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.jbi.2012.04.008","volume":"45","author":"H Gurulingappa","year":"2012","unstructured":"Gurulingappa H, Rajput AM, Roberts A, Fluck J, Hofmann-Apitius M, Toldo L (2012) Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. J Biomed Inform 45(5):885\u2013892. https:\/\/doi.org\/10.1016\/j.jbi.2012.04.008","journal-title":"J Biomed Inform"},{"issue":"5","key":"10484_CR13","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.jbi.2013.07.011","volume":"46","author":"M Herrero-Zazo","year":"2013","unstructured":"Herrero-Zazo M, Segura-Bedmar I, Mart\u00ednez P, Declerck T (2013) The DDI corpus: an annotated corpus with pharmacological substances and drug\u2013drug interactions. J Biomed Inform 46(5):914\u2013920. https:\/\/doi.org\/10.1016\/j.jbi.2013.07.011","journal-title":"J Biomed Inform"},{"issue":"6","key":"10484_CR14","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1038\/s42256-020-0189-y","volume":"2","author":"L Hong","year":"2020","unstructured":"Hong L, Lin J, Li S, Wan F, Yang H, Jiang T, Zhao D, Zeng J (2020) A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. Nat Mach Intell 2(6):347\u2013355. https:\/\/doi.org\/10.1038\/s42256-020-0189-y","journal-title":"Nat Mach Intell"},{"issue":"17","key":"10484_CR15","doi-asserted-by":"publisher","first-page":"6325","DOI":"10.1002\/cpe.6325","volume":"33","author":"A Hosseinalipour","year":"2021","unstructured":"Hosseinalipour A, Gharehchopogh FS, Masdari M, Khademi A (2021) Toward text psychology analysis using social spider optimization algorithm. Concurr Comput 33(17):6325. https:\/\/doi.org\/10.1002\/cpe.6325","journal-title":"Concurr Comput"},{"issue":"7","key":"10484_CR16","doi-asserted-by":"publisher","first-page":"4824","DOI":"10.1007\/s10489-020-02038-y","volume":"51","author":"A Hosseinalipour","year":"2021","unstructured":"Hosseinalipour A, Gharehchopogh FS, Masdari M, Khademi A (2021) A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Appl Intell 51(7):4824\u20134859. https:\/\/doi.org\/10.1007\/s10489-020-02038-y","journal-title":"Appl Intell"},{"issue":"6","key":"10484_CR17","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.ijmedinf.2005.06.010","volume":"75","author":"M Huang","year":"2006","unstructured":"Huang M, Zhu X, Li M (2006) A hybrid method for relation extraction from biomedical literature. Int J Med Inf 75(6):443\u2013455. https:\/\/doi.org\/10.1016\/j.ijmedinf.2005.06.010","journal-title":"Int J Med Inf"},{"issue":"3","key":"10484_CR18","doi-asserted-by":"publisher","first-page":"488","DOI":"10.3390\/math10030488","volume":"10","author":"H Khataei Maragheh","year":"2022","unstructured":"Khataei Maragheh H, Gharehchopogh FS, Majidzadeh K, Sangar AB (2022) A new hybrid based on long short-term memory network with spotted hyena optimization algorithm for multi-label text classification. Mathematics 10(3):488. https:\/\/doi.org\/10.3390\/math10030488","journal-title":"Mathematics"},{"issue":"8","key":"10484_CR19","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s12859-018-2200-8","volume":"19","author":"M Kim","year":"2018","unstructured":"Kim M, Baek SH, Song M (2018) Relation extraction for biological pathway construction using node2vec. BMC Bioinform 19(8):75\u201384. https:\/\/doi.org\/10.1186\/s12859-018-2200-8","journal-title":"BMC Bioinform"},{"key":"10484_CR20","unstructured":"Koch G, Zemel R, Salakhutdinov R, et al (2015) Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol 2, p.0.Lille. https:\/\/www.cs.cmu.edu\/rsalakhu\/papers\/oneshot1.pdf"},{"key":"10484_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/database\/bav123","author":"J Kringelum","year":"2016","unstructured":"Kringelum J, Kj\u00e6rulff SK, Brunak S, Lund O, Oprea TI, Taboureau O (2016) Chemprot-3.0: a global chemical biology diseases mapping. Database J Biol Databases Curation. https:\/\/doi.org\/10.1093\/database\/bav123","journal-title":"Database J Biol Databases Curation"},{"issue":"4","key":"10484_CR22","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234\u20131240. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","journal-title":"Bioinformatics"},{"key":"10484_CR23","doi-asserted-by":"publisher","unstructured":"Lee C, Hwang Y, Jang M (2007) Fine-grained named entity recognition and relation extraction for question answering. In: Kraaij W, de Vries AP, Clarke CLA, Fuhr N, Kando N (eds) SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, Amsterdam, The Netherlands, July 23\u201327, 2007, pp 799\u2013800. ACM. https:\/\/doi.org\/10.1145\/1277741.1277915","DOI":"10.1145\/1277741.1277915"},{"key":"10484_CR24","doi-asserted-by":"publisher","unstructured":"Lewis P, Ott M, Du J, Stoyanov V (2020) Pretrained language models for biomedical and clinical tasks: Understanding and extending the state-of-the-art. In: Proceedings of the 3rd clinical natural language processing workshop, pp 146\u2013157. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.clinicalnlp-1.17","DOI":"10.18653\/v1\/2020.clinicalnlp-1.17"},{"issue":"1","key":"10484_CR25","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/s12859-017-1609-9","volume":"18","author":"F Li","year":"2017","unstructured":"Li F, Zhang M, Fu G, Ji D (2017) A neural joint model for entity and relation extraction from biomedical text. BMC Bioinform 18(1):198\u2013119811. https:\/\/doi.org\/10.1186\/s12859-017-1609-9","journal-title":"BMC Bioinform"},{"key":"10484_CR26","doi-asserted-by":"publisher","unstructured":"Li Q, Yang Z, Luo L, Wang L, Zhang Y, Lin H, Wang J, Yang L, Xu K, Zhang Y (2018) A multi-task learning based approach to biomedical entity relation extraction. In: Zheng HJ, Callejas Z, Griol D, Wang H, Hu X, Schmidt HHHW, Baumbach J, Dickerson J, Zhang L (eds) IEEE international conference on bioinformatics and biomedicine, BIBM 2018, Madrid, Spain, December 3\u20136, 2018, pp 680\u2013682. IEEE Computer Society. https:\/\/doi.org\/10.1109\/BIBM.2018.8621284","DOI":"10.1109\/BIBM.2018.8621284"},{"key":"10484_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110345","author":"C Li","year":"2023","unstructured":"Li C, Li S, Wang H, Gu F, Ball AD (2023) Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2023.110345","journal-title":"Knowl-Based Syst"},{"key":"10484_CR28","unstructured":"Liu X, He P, Chen W, Gao J (2019) Improving multi-task deep neural networks via knowledge distillation for natural language understanding. CoRR arXiv:1904.09482"},{"key":"10484_CR29","doi-asserted-by":"publisher","unstructured":"Liu X, He P, Chen W, Gao J (2019) Multi-task deep neural networks for natural language understanding. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1441","DOI":"10.18653\/v1\/P19-1441"},{"key":"10484_CR30","doi-asserted-by":"crossref","unstructured":"Liu X, He P, Chen W, Gao J (2019) Multi-task deep neural networks for natural language understanding. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 4487\u20134496. Association for Computational Linguistics, Florence, Italy. https:\/\/www.aclweb.org\/anthology\/P19-1441","DOI":"10.18653\/v1\/P19-1441"},{"issue":"6","key":"10484_CR31","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1007\/s41870-022-01049-x","volume":"14","author":"ER Mahalleh","year":"2022","unstructured":"Mahalleh ER, Gharehchopogh FS (2022) An automatic text summarization based on valuable sentences selection. Int J Inf Technol 14(6):2963\u20132969. https:\/\/doi.org\/10.1007\/s41870-022-01049-x","journal-title":"Int J Inf Technol"},{"issue":"1","key":"10484_CR32","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s12859-022-04646-6","volume":"23","author":"S Marchesin","year":"2022","unstructured":"Marchesin S, Silvello G (2022) TBGA: a large-scale gene-disease association dataset for biomedical relation extraction. BMC Bioinform 23(1):111. https:\/\/doi.org\/10.1186\/s12859-022-04646-6","journal-title":"BMC Bioinform"},{"key":"10484_CR33","unstructured":"Nebhi K (2013) A rule-based relation extraction system using dbpedia and syntactic parsing. In: Hellmann S, Filipowska A, Barri\u00e8re C, Mendes PN, Kontokostas D (eds) Proceedings of the NLP & dbpedia workshop co-located with the 12th international semantic web conference (ISWC 2013), Sydney, Australia, October 22, 2013. CEUR Workshop Proceedings, vol 1064. CEUR-WS.org, Online. http:\/\/ceur-ws.org\/Vol-1064\/Nebhi_Rule-Based.pdf"},{"key":"10484_CR34","doi-asserted-by":"publisher","unstructured":"Peng Y, Yan S, Lu Z (2019) Transfer learning in biomedical natural language processing: An evaluation of BERT and elmo on ten benchmarking datasets. In: Demner-Fushman, D., Cohen, K.B., Ananiadou, S., Tsujii, J. (eds.) Proceedings of the 18th BioNLP Workshop and Shared Task, BioNLP@ACL 2019, Florence, Italy, August 1, 2019, pp. 58\u201365. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/w19-5006","DOI":"10.18653\/v1\/w19-5006"},{"key":"10484_CR35","doi-asserted-by":"publisher","unstructured":"Peng Y, Chen Q, Lu Z (2020) An empirical study of multi-task learning on BERT for biomedical text mining. In: Proceedings of the 19th SIGBioMed workshop on biomedical language processing. Association for Computational Linguistics. pp 205\u2013214. https:\/\/doi.org\/10.18653\/v1\/2020.bionlp-1.22","DOI":"10.18653\/v1\/2020.bionlp-1.22"},{"key":"10484_CR36","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/1471-2105-8-50","volume":"8","author":"S Pyysalo","year":"2006","unstructured":"Pyysalo S, Ginter F, Heimonen J, Bj\u00f6rne J, Boberg J, J\u00e4rvinen J, Salakoski T (2006) Bioinfer: a corpus for information extraction in the biomedical domain. BMC Bioinform 8:50\u201350. https:\/\/doi.org\/10.1186\/1471-2105-8-50","journal-title":"BMC Bioinform"},{"key":"10484_CR37","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 3980\u20133990. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"10484_CR38","doi-asserted-by":"publisher","unstructured":"Schick T, Sch\u00fctze H (2021) Exploiting cloze-questions for few-shot text classification and natural language inference. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, pp. 255\u2013269. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.20","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"10484_CR39","unstructured":"Snell J, Swersky K, Zemel RS (2017) Prototypical networks for few-shot learning. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp 4077\u20134087. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/cb8da6767461f2812ae4290eac7cbc42-Abstract.html"},{"key":"10484_CR40","unstructured":"Standley T, Zamir A, Chen D, Guibas LJ, Malik J, Savarese S (2020) Which tasks should be learned together in multi-task learning? In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol. 119, pp. 9120\u20139132. PMLR, Online. http:\/\/proceedings.mlr.press\/v119\/standley20a.html"},{"key":"10484_CR41","doi-asserted-by":"publisher","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PHS, Hospedales TM (2018) Learning to compare: relation network for few-shot learning. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18\u201322, 2018. Computer vision foundation\/IEEE Computer Society. pp 1199\u20131208 https:\/\/doi.org\/10.1109\/CVPR.2018.00131","DOI":"10.1109\/CVPR.2018.00131"},{"key":"10484_CR42","doi-asserted-by":"publisher","unstructured":"Tang H, Li Z, Peng Z, Tang J (2020) Blockmix: meta regularization and self-calibrated inference for metric-based meta-learning. In: Chen CW, Cucchiara R, Hua X, Qi G, Ricci E, Zhang Z, Zimmermann R (eds) MM \u201920: The 28th ACM international conference on multimedia, virtual event\/Seattle, WA, USA, October 12\u201316, 2020, pp. 610\u2013618. ACM. https:\/\/doi.org\/10.1145\/3394171.3413884","DOI":"10.1145\/3394171.3413884"},{"issue":"5","key":"10484_CR43","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1136\/amiajnl-2011-000203","volume":"18","author":"\u00d6 Uzuner","year":"2011","unstructured":"Uzuner \u00d6, South BR, Shen S, DuVall SL (2011) 2010 i2b2\/va challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 18(5):552\u2013556. https:\/\/doi.org\/10.1136\/amiajnl-2011-000203","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"10484_CR44","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743. https:\/\/doi.org\/10.1109\/TKDE.2017.2754499","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10484_CR45","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.patrec.2020.05.031","volume":"136","author":"J Worsham","year":"2020","unstructured":"Worsham J, Kalita J (2020) Multi-task learning for natural language processing in the 2020s: Where are we going? Pattern Recognit. Lett. 136:120\u2013126. https:\/\/doi.org\/10.1016\/j.patrec.2020.05.031","journal-title":"Pattern Recognit. Lett."},{"key":"10484_CR46","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.jbi.2018.03.011","volume":"81","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Lin H, Yang Z, Wang J, Zhang S, Sun Y, Yang L (2018) A hybrid model based on neural networks for biomedical relation extraction. J Biomed Inform 81:83\u201392. https:\/\/doi.org\/10.1016\/j.jbi.2018.03.011","journal-title":"J Biomed Inform"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10484-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10484-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10484-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T03:53:31Z","timestamp":1695441211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10484-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":46,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10484"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10484-6","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]},"assertion":[{"value":"19 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}