{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:05:24Z","timestamp":1743087924624,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819972531"},{"type":"electronic","value":"9789819972548"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-7254-8_59","type":"book-chapter","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T05:01:47Z","timestamp":1697864507000},"page":"763-777","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CREAM: Named Entity Recognition with\u00a0Concise query and\u00a0REgion-Aware Minimization"],"prefix":"10.1007","author":[{"given":"Xun","family":"Yao","sequence":"first","affiliation":[]},{"given":"Qihang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xinrong","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"59_CR1","unstructured":"Collier, N., Ohta, T., Tsuruoka, Y., Tateisi, Y., Kim, J.D.: Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA\/BioNLP), pp. 73\u201378. COLING, Geneva, Switzerland (2004)"},{"key":"59_CR2","unstructured":"Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R.: The automatic content extraction (ACE) program - tasks, data, and evaluation. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC\u201904). European Language Resources Association (ELRA), Lisbon, Portugal (2004)"},{"key":"59_CR3","doi-asserted-by":"crossref","unstructured":"Fu, J., Huang, X., Liu, P.: SpanNER: named entity re-\/recognition as span prediction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, pp. 7183\u20137195. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.558"},{"key":"59_CR4","doi-asserted-by":"crossref","unstructured":"Huang, P., Zhao, X., Hu, M., Fang, Y., Li, X., Xiao, W.: Extract-select: a span selection framework for nested named entity recognition with generative adversarial training. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 85\u201396. Association for Computational Linguistics, Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.findings-acl.9"},{"issue":"5","key":"59_CR5","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429\u2013449 (2002)","journal-title":"Intell. Data Anal."},{"key":"59_CR6","doi-asserted-by":"crossref","unstructured":"Katiyar, A., Cardie, C.: Nested named entity recognition revisited. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 861\u2013871. Association for Computational Linguistics, New Orleans, Louisiana (2018)","DOI":"10.18653\/v1\/N18-1079"},{"key":"59_CR7","doi-asserted-by":"crossref","unstructured":"Li, F., Lin, Z., Zhang, M., Ji, D.: A span-based model for joint overlapped and discontinuous 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 (Volume 1: Long Papers), pp. 4814\u20134828. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.372"},{"key":"59_CR8","doi-asserted-by":"crossref","unstructured":"Li, F., et al.: Modularized interaction network 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 (Volume 1: Long Papers), pp. 200\u2013209. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.17"},{"key":"59_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., Li, J.: A unified MRC framework for named entity recognition. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5849\u20135859. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.519"},{"key":"59_CR10","doi-asserted-by":"crossref","unstructured":"Liu, J., Mei, S., Hu, X., Yao, X., Yang, J., Guo, Y.: Seeing the wood for the trees: a contrastive regularization method for the low-resource knowledge base question answering. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 1085\u20131094. Association for Computational Linguistics, Seattle, United States (2022)","DOI":"10.18653\/v1\/2022.findings-naacl.82"},{"key":"59_CR11","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach, vol. abs\/1907.11692 (2019)"},{"key":"59_CR12","doi-asserted-by":"crossref","unstructured":"Long, X., Niu, S., Li, Y.: Hierarchical region learning for nested named entity recognition. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4788\u20134793. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.430"},{"key":"59_CR13","doi-asserted-by":"crossref","unstructured":"Lou, C., Yang, S., Tu, K.: Nested named entity recognition as latent lexicalized constituency parsing. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6183\u20136198. Association for Computational Linguistics, Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.acl-long.428"},{"key":"59_CR14","doi-asserted-by":"crossref","unstructured":"Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1064\u20131074. Association for Computational Linguistics, Berlin, Germany (2016)","DOI":"10.18653\/v1\/P16-1101"},{"key":"59_CR15","unstructured":"Medero, J., Maeda, K., Strassel, S., Walker, C.: An efficient approach to gold-standard annotation: decision points for complex tasks. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC\u201906). European Language Resources Association (ELRA), Genoa, Italy (2006)"},{"key":"59_CR16","doi-asserted-by":"crossref","unstructured":"Muis, A.O., Lu, W.: Labeling gaps between words: recognizing overlapping mentions with mention separators. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2608\u20132618. Association for Computational Linguistics, Copenhagen, Denmark (2017)","DOI":"10.18653\/v1\/D17-1276"},{"key":"59_CR17","doi-asserted-by":"crossref","unstructured":"Ohta, T., Tateisi, Y., Kim, J.D.: The GENIA corpus: an annotated research abstract corpus in molecular biology domain. In: International Conference on Human Language Technology Research (2002)","DOI":"10.3115\/1289189.1289260"},{"key":"59_CR18","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ma, X., Tan, Z., Zhang, S., Wang, W., Lu, W.: Locate and label: A two-stage identifier for nested 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 (Volume 1: Long Papers), pp. 2782\u20132794. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.216"},{"key":"59_CR19","doi-asserted-by":"crossref","unstructured":"Shrimal, A., Jain, A., Mehta, K., Yenigalla, P.: NER-MQMRC: formulating named entity recognition as multi question machine reading comprehension. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pp. 230\u2013238. Association for Computational Linguistics, Hybrid: Seattle, Washington + Online (2022)","DOI":"10.18653\/v1\/2022.naacl-industry.26"},{"key":"59_CR20","doi-asserted-by":"crossref","unstructured":"Tan, Z., Shen, Y., Zhang, S., Lu, W., Zhuang, Y.: A sequence-to-set network for nested named entity recognition. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI-21 (2021)","DOI":"10.24963\/ijcai.2021\/542"},{"key":"59_CR21","doi-asserted-by":"crossref","unstructured":"Tjong Kim Sang, E.F., De Meulder, 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 2003, pp. 142\u2013147 (2003)","DOI":"10.3115\/1119176.1119195"},{"key":"59_CR22","doi-asserted-by":"crossref","unstructured":"Verlinden, S., Zaporojets, K., Deleu, J., Demeester, T., Develder, C.: Injecting knowledge base information into end-to-end joint entity and relation extraction and coreference resolution. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1952\u20131957. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-acl.171"},{"key":"59_CR23","doi-asserted-by":"crossref","unstructured":"Wan, J., Ru, D., Zhang, W., Yu, Y.: Nested named entity recognition with span-level graphs. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 892\u2013903. Association for Computational Linguistics, Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.acl-long.63"},{"key":"59_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: MINER: improving out-of-vocabulary named entity recognition from an information theoretic perspective. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5590\u20135600. Association for Computational Linguistics, Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.acl-long.383"},{"key":"59_CR25","doi-asserted-by":"crossref","unstructured":"Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., Qiu, X.: A unified generative framework for various NER subtasks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5808\u20135822. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.451"},{"key":"59_CR26","unstructured":"Yun, C., Bhojanapalli, S., Rawat, A.S., Reddi, S.J., Kumar, S.: Are transformers universal approximators of sequence-to-sequence functions? CoRR abs\/1912.10077 (2019)"},{"key":"59_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Fu, J., Liu, X., Huang, X.: Adaptive co-attention network for named entity recognition in tweets. Proc, AAAI Conf. Artif. Intell. 32(1) (2018)","DOI":"10.1609\/aaai.v32i1.11962"},{"key":"59_CR28","doi-asserted-by":"crossref","unstructured":"Zhu, E., Li, J.: Boundary smoothing for named entity recognition. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7096\u20137108. Association for Computational Linguistics, Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.acl-long.490"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7254-8_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T15:10:52Z","timestamp":1730387452000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7254-8_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819972531","9789819972548"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7254-8_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.wise-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"137","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}