{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:40:16Z","timestamp":1760740816466,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819534555"},{"type":"electronic","value":"9789819534562"}],"license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"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\/978-981-95-3456-2_20","type":"book-chapter","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:23:05Z","timestamp":1760599385000},"page":"287-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Deep AUC Maximisation for\u00a0Enhanced Active Learning in\u00a0Named Entity Recognition"],"prefix":"10.1007","author":[{"given":"Wei","family":"Tan","sequence":"first","affiliation":[]},{"given":"Dan","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wray","family":"Buntine","sequence":"additional","affiliation":[]},{"given":"Haifeng","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Lan","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"20_CR1","unstructured":"Ash, J.T., Zhang, C., Krishnamurthy, A., Langford, J., Agarwal, A.: Deep batch active learning by diverse, uncertain gradient lower bounds. In: Proc. 8th International Conference on Learning Representations (2020)"},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JP Chiu","year":"2016","unstructured":"Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Tran. Associat. Comput. Linguist. 4, 357\u2013370 (2016)","journal-title":"Tran. Associat. Comput. Linguist."},{"key":"20_CR3","unstructured":"Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators. In: International Conference on Learning Representations (2020)"},{"key":"20_CR4","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, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)"},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","volume":"47","author":"RI Do\u011fan","year":"2014","unstructured":"Do\u011fan, R.I., Leaman, R., Lu, Z.: Special report: NCBI disease corpus: A resource for disease name recognition and concept normalization. J. of Biomed. Inform. 47, 1\u201310 (2014)","journal-title":"J. of Biomed. Inform."},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Dredze, M., Crammer, K.: Active learning with confidence. In: Proceedings of ACL 2008: HLT, Short Papers, pp. 233\u2013236 (2008)","DOI":"10.3115\/1557690.1557757"},{"key":"20_CR7","first-page":"933","volume":"4","author":"Y Freund","year":"2003","unstructured":"Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933\u2013969 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR8","unstructured":"Gao, W., Jin, R., Zhu, S., Zhou, Z.H.: One-pass AUC optimization. In: International Conference on Machine Learning, pp. 906\u2013914. PMLR (2013)"},{"key":"20_CR9","unstructured":"Houlsby, N., Husz\u00e1r, F., Ghahramani, Z., Lengyel, M.: Bayesian active learning for classification and preference learning. arXiv preprint arXiv:1112.5745 (2011)"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Woodland, P.C.: A rule-based named entity recognition system for speech input. In: Proc. ICSLP 2000.,pp. vol\u20131 (2000)","DOI":"10.21437\/ICSLP.2000-131"},{"key":"20_CR11","unstructured":"Kripke, S.: Naming and Necessity. Harvard University Press (1980)"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 260\u2013270 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"issue":"4","key":"20_CR13","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2019","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2019)","journal-title":"Bioinformatics"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Liu, M., Tu, Z., Zhang, T., Su, T., Xu, X., Wang, Z.: LTP: a new active learning strategy for CRF-based named entity recognition. Neural Process. Lett. 54(3), 2433\u20132454 (2022)","DOI":"10.1007\/s11063-021-10737-x"},{"key":"20_CR15","unstructured":"Liu, Y., et al.: Roberta: A robustly optimized bert pretraining approach (2020)"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Lowell, D., Lipton, Z.C., Wallace, B.C.: Practical obstacles to deploying active learning. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 21\u201330 (2019)","DOI":"10.18653\/v1\/D19-1003"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Nguyen, N.D., Du, L., Buntine, W., Chen, C., Beare, R.: Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 4063\u20134071 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.271"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Nguyen, N.D., Tan, W., Du, L., Buntine, W., Beare, R., Chen, C.: Auc maximization for low-resource named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 13389\u201313399 (2023)","DOI":"10.1609\/aaai.v37i11.26571"},{"key":"20_CR19","doi-asserted-by":"publisher","unstructured":"Nguyen, N.D., Tan, W., Du, L., Buntine, W., Beare, R., Chen, C.: Low-resource named entity recognition: Can one-vs-all auc maximization help? In: 2023 IEEE International Conference on Data Mining (ICDM), pp. 1241\u20131246 (2023). https:\/\/doi.org\/10.1109\/ICDM58522.2023.00155","DOI":"10.1109\/ICDM58522.2023.00155"},{"issue":"1","key":"20_CR20","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10994-021-06003-9","volume":"111","author":"VL Nguyen","year":"2022","unstructured":"Nguyen, V.L., Shaker, M.H., H\u00fcllermeier, E.: How to measure uncertainty in uncertainty sampling for active learning. Mach. Learn. 111(1), 89\u2013122 (2022)","journal-title":"Mach. Learn."},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Prabhu, A., Dognin, C., Singh, M.: Sampling bias in deep active classification: an empirical study. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4058\u20134068 (2019)","DOI":"10.18653\/v1\/D19-1417"},{"key":"20_CR22","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, pp. 4310\u20134321 (2021)","DOI":"10.18653\/v1\/2021.acl-long.332"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Ren, P., et al.: A survey of deep active learning. ACM Comput. Surv. 54(9) (2021)","DOI":"10.1145\/3472291"},{"key":"20_CR24","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)"},{"key":"20_CR25","unstructured":"Schr\u00f6der, C., Niekler, A.: A survey of active learning for text classification using deep neural networks. arXiv preprint arXiv:2008.07267 (2020)"},{"key":"20_CR26","unstructured":"Settles, B.: Active learning literature survey. Computer Sciences Technical Report\u00a01648, Univ. of Wisconsin\u2013Madison (2009)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Shelmanov, A., et al.: Active learning for sequence tagging with deep pre-trained models and Bayesian uncertainty estimates. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 1698\u20131712 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.145"},{"key":"20_CR28","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 (2017)","DOI":"10.18653\/v1\/W17-2630"},{"key":"20_CR29","unstructured":"Tan, W., Du, L., Buntine, W.: Diversity enhanced active learning with strictly proper scoring rules. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 10906\u201310918. Curran Associates, Inc. (2021)"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Tjong Kim\u00a0Sang, E.F., De\u00a0Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL, pp. 142\u2013147 (2003)","DOI":"10.3115\/1119176.1119195"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Wei, C.H., et al.: Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task. Database 2016 (2016)","DOI":"10.1093\/database\/baw032"},{"key":"20_CR32","unstructured":"Weischedel, R., et\u00a0al.: Ontonotes release 5.0. LDC2011T03, Philadelphia, Penn.: Linguistic Data Consortium (2014)"},{"key":"20_CR33","unstructured":"Yadav, V., Bethard, S.: A survey on recent advances in named entity recognition from deep learning models. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2145\u20132158 (Aug 2018)"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Yuan, M., Lin, H.T., Boyd-Graber, J.: Cold-start active learning through self-supervised language modeling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7935\u20137948 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.637"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Yan, Y., Sonka, M., Yang, T.: Large-scale robust deep AUC maximization: a new surrogate loss and empirical studies on medical image classification. In: 2021 IEEE\/CVF International Conference on Computer Vision, pp. 3020\u20133029 (2021)","DOI":"10.1109\/ICCV48922.2021.00303"},{"key":"20_CR36","unstructured":"Yuan, Z., Guo, Z., Chawla, N., Yang, T.: Compositional training for end-to-end deep AUC maximization. In: Proceedings of International Conference on Learning Representations (2021)"},{"key":"20_CR37","unstructured":"Zhao, G., Dougherty, E., Yoon, B.J., Alexander, F., Qian, X.: Uncertainty-aware active learning for optimal Bayesian classifier. In: Proceedings of International Conference on Learning Representations (2021)"},{"key":"20_CR38","unstructured":"Zhao, P., Hoi, S.C.H., Jin, R., Yang, T.: Online AUC maximization. In: Proceedings of the 28th International Conference on International Conference on Machine Learning, p. 233\u2013240 (2011)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3456-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:04:19Z","timestamp":1760738659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3456-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"ISBN":["9789819534555","9789819534562"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3456-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"17 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"22 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}