{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:08:25Z","timestamp":1760400505615,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032084613"},{"type":"electronic","value":"9783032084620"}],"license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-08462-0_25","type":"book-chapter","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:47:19Z","timestamp":1760330839000},"page":"315-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perplexity, Uncertainty, and\u00a0the\u00a0Limits of\u00a0Active Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5563-1120","authenticated-orcid":false,"given":"Pablo","family":"Tur\u00f3n","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3620-1053","authenticated-orcid":false,"given":"Montse","family":"Cuadros","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Brantley, K., Sharaf, A., Daum\u00e9\u00a0III, H.: Active imitation learning with noisy guidance. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2093\u20132105. Association for Computational Linguistics, Online (2020)","DOI":"10.18653\/v1\/2020.acl-main.189"},{"key":"25_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102062","volume":"71","author":"S Budd","year":"2021","unstructured":"Budd, S., Robinson, E.C., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal. 71, 102062 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"25_CR3","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/TNNLS.2020.3027605","volume":"33","author":"X Cao","year":"2022","unstructured":"Cao, X., Tsang, I.W.: Shattering distribution for active learning. IEEE Trans. Neural Netw. Learn. Syst. 33(1), 215\u2013228 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"25_CR4","unstructured":"Citovsky, G., et al.: Batch active learning at scale. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. NIPS \u201921, Curran Associates Inc., Red Hook, NY, USA (2021)"},{"key":"25_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Fang, M., Li, Y., Cohn, T.: Learning how to active learn: a deep reinforcement learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 595\u2013605. Association for Computational Linguistics, Copenhagen, Denmark (2017)","DOI":"10.18653\/v1\/D17-1063"},{"issue":"5","key":"25_CR7","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1136\/amiajnl-2011-000648","volume":"19","author":"RL Figueroa","year":"2012","unstructured":"Figueroa, R.L., Zeng-Treitler, Q., Ngo, L.H., Goryachev, S., Wiechmann, E.P.: Active learning for clinical text classification: is it better than random sampling? J. Am. Med. Inf. Assoc. 19(5), 809\u2013816 (2012)","journal-title":"J. Am. Med. Inf. Assoc."},{"issue":"2","key":"25_CR8","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1023\/A:1007330508534","volume":"28","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 28(2), 133\u2013168 (1997)","journal-title":"Mach. Learn."},{"key":"25_CR9","unstructured":"Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: Proceedings of the 34th International Conference on Machine Learning , vol. 70, pp. 1183\u20131192. ICML\u201917, JMLR.org (2017)"},{"issue":"9","key":"25_CR10","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.1109\/TNNLS.2020.3016928","volume":"32","author":"B Gu","year":"2021","unstructured":"Gu, B., Zhai, Z., Deng, C., Huang, H.: Efficient active learning by querying discriminative and representative samples and fully exploiting unlabeled data. IEEE Trans. Neural Netw. Learn. Syst. 32(9), 4111\u20134122 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"25_CR11","unstructured":"Hadian, H., Sameti, H.: Active learning in noisy conditions for spoken language understanding. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 1081\u20131090. Dublin City University and Association for Computational Linguistics, Dublin, Ireland (2014)"},{"key":"25_CR12","unstructured":"Hasan, M., Paul, S., Mourikis, A., Roy-Chowdhury, A.: Context-aware query selection for active learning in event recognition. IEEE Trans. Patt. Analy. Mach. Intell. PP, 1\u20131 (2018)"},{"issue":"10","key":"25_CR13","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1109\/TPAMI.2014.2307881","volume":"36","author":"SJ Huang","year":"2014","unstructured":"Huang, S.J., Jin, R., Zhou, Z.H.: Active learning by querying informative and representative examples. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 1936\u20131949 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR14","unstructured":"la\u00a0Iglesia, I.D., Atutxa, A., Gojenola, K., Barrena, A.: EriBERTa: a bilingual pre-trained language model for clinical natural language processing (2023)"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: SIGIR \u201994, pp. 3\u201312. Springer London, London (1994)","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"25_CR16","unstructured":"Li, W., Dasarathy, G., Ramamurthy, K.N., Berisha, V.: Finding the homology of decision boundaries with active learning. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS \u201920, Curran Associates Inc., Red Hook, NY, USA (2020)"},{"key":"25_CR17","unstructured":"Lima-L\u00f3pez, S., et al.: Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. In: Working Notes of CLEF 2023 \u2013 Conference and Labs of the Evaluation Forum (2023)"},{"key":"25_CR18","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized bert pretraining approach. arxiv abs\/1907.11692 (2019)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Lowell, D., Lipton, Z., Wallace, B.: Practical obstacles to deploying active learning, pp. 21\u201330 (2019)","DOI":"10.18653\/v1\/D19-1003"},{"key":"25_CR20","unstructured":"Malmasi, S., Fang, A., Fetahu, B., Kar, S., Rokhlenko, O.: MultiCoNER: a large-scale multilingual dataset for complex named entity recognition. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 3798\u20133809. International Committee on Computational Linguistics, Gyeongju, Republic of Korea (2022)"},{"issue":"29","key":"25_CR21","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes, L., Healy, J., Saul, N., Gro\u00dfberger, L.: UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018)","journal-title":"J. Open Source Softw."},{"key":"25_CR22","unstructured":"Miranda-Escalada, A., Farr\u00e9-Maduell, E., Lima-L\u00f3pez, S., Estrada, D., Gasc\u00f3, L., Krallinger, M.: Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources. Procesamiento del Lenguaje Natural (2022)"},{"key":"25_CR23","unstructured":"OpenAI: ChatGPT (may 26 version) (2025), Accessed 26 May 2025"},{"key":"25_CR24","first-page":"233","volume":"67","author":"A Piad-Morfis","year":"2021","unstructured":"Piad-Morfis, A., Estevez-Velarde, S., Gutierrez, Y., Almeida-Cruz, Y., Montoyo, A., Mu\u00f1oz, R.: Overview of the eHealth knowledge discovery challenge at IberLEF 2021. Procesamiento del Lenguaje Natural 67, 233\u2013242 (2021)","journal-title":"Procesamiento del Lenguaje Natural"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Radmard, P., Fathullah, Y., Lipani, A.: Subsequence based deep active learning for named entity recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1: Long Papers), pp. 4310\u20134321. Association for Computational Linguistics, Online (2021)","DOI":"10.18653\/v1\/2021.acl-long.332"},{"key":"25_CR26","unstructured":"Raffel, C., et al.: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2023)"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Schr\u00f6der, C., Niekler, A., Potthast, M.: Revisiting uncertainty-based query strategies for active learning with transformers. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 2194\u20132203. Association for Computational Linguistics, Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.findings-acl.172"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 747\u2013748 (2020)","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Shen, Y., Yun, H., Lipton, Z., Kronrod, Y., Anandkumar, A.: Deep active learning for named entity recognition. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, pp. 252\u2013256. Association for Computational Linguistics, Vancouver, Canada (2017)","DOI":"10.18653\/v1\/W17-2630"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Shi, T., Benton, A., Malioutov, I., \u0130rsoy, O.: Diversity-aware batch active learning for dependency parsing. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2616\u20132626. Association for Computational Linguistics, Online (2021)","DOI":"10.18653\/v1\/2021.naacl-main.207"},{"key":"25_CR31","unstructured":"SNOMED International: SNOMED Clinical Terms (SNOMED CT) International Edition (2025). https:\/\/www.snomed.org\/snomed-ct"},{"key":"25_CR32","unstructured":"Thiessen, M., Gaertner, T.: Active learning of convex halfspaces on graphs. In: Advances in Neural Information Processing Systems. vol.\u00a034, pp. 23413\u201323425. Curran Associates, Inc. (2021)"},{"key":"25_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need, pp. 6000\u20136010. NIPS\u201917, Curran Associates Inc., Red Hook, NY, USA (2017)"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Vu, T.T., Liu, M., Phung, D., Haffari, G.: Learning how to active learn by dreaming. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4091\u20134101. Association for Computational Linguistics, Florence, Italy (2019)","DOI":"10.18653\/v1\/P19-1401"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Wang, D., Shang, Y.: A new active labeling method for deep learning. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 112\u2013119 (2014)","DOI":"10.1109\/IJCNN.2014.6889457"},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE Trans. Cir. and Sys. for Video Technol. 27(12), 2591\u20132600 (2017)","DOI":"10.1109\/TCSVT.2016.2589879"},{"key":"25_CR37","unstructured":"Wang, Z., Chen, Y., Jiang, R., Ding, W., Okumura, M.: A survey on deep active learning: recent advances and new frontiers. IEEE Trans. Neural Netw. Learn. Syst. PP (2024)"},{"key":"25_CR38","doi-asserted-by":"crossref","unstructured":"Xie, B., Yuan, L., Li, S., Liu, C.H., Cheng, X., Wang, G.: Active learning for domain adaptation: an energy-based approach (2022)","DOI":"10.1609\/aaai.v36i8.20850"},{"key":"25_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108836","volume":"131","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Loog, M.: To actively initialize active learning. Patt. Recogn. 131, 108836 (2022)","journal-title":"Patt. Recogn."},{"key":"25_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Strubell, E., Hovy, E.: A survey of active learning for natural language processing. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 6166\u20136190. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.414"},{"key":"25_CR41","unstructured":"Zhao, G., Dougherty, E., Yoon, B.J., Alexander, F., Qian, X.: Efficient active learning for gaussian process classification by error reduction. In: Advances in Neural Information Processing Systems. vol.\u00a034, pp. 9734\u20139746. Curran Associates, Inc. (2021)"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08462-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T05:03:19Z","timestamp":1760331799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08462-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"ISBN":["9783032084613","9783032084620"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08462-0_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"14 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/haisconference.eu","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}