{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:34:23Z","timestamp":1743039263563,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811961342"},{"type":"electronic","value":"9789811961359"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-6135-9_26","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T14:04:46Z","timestamp":1666274686000},"page":"345-358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Span-Based Joint Model for Measurable Quantitative Information Extraction"],"prefix":"10.1007","author":[{"given":"Di","family":"Mo","sequence":"first","affiliation":[]},{"given":"Bangrui","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Weng","sequence":"additional","affiliation":[]},{"given":"Tianyong","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"unstructured":"Hao, T., We, Y., Qiang, J., Wang, H., Lee, K.: The representation and extraction of quantitative information. In: Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13) (2017)","key":"26_CR1"},{"issue":"7","key":"26_CR2","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1002\/pds.4204","volume":"26","author":"A Maguire","year":"2017","unstructured":"Maguire, A., Johnson, M.E., Denning, D.W., Ferreira, G.L.C., Cassidy, A.: Identifying rare diseases using electronic medical records: the example of allergic bronchopulmonary aspergillosis. Pharmacoepidemiol. Drug Saf. 26(7), 785\u2013791 (2017)","journal-title":"Pharmacoepidemiol. Drug Saf."},{"issue":"5","key":"26_CR3","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.amjmed.2016.12.008","volume":"130","author":"DW Frost","year":"2017","unstructured":"Frost, D.W., Vembu, S., Wang, J., Tu, K., Morris, Q., Abrams, H.B.: Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs. Am. J. Med. 130(5), 601-e17 (2017)","journal-title":"Am. J. Med."},{"doi-asserted-by":"crossref","unstructured":"Lossio-Ventura, J.A., et al.: Towards an obesity-cancer knowledge base: Biomedical entity identification and relation detection. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1081\u20131088 (2016)","key":"26_CR4","DOI":"10.1109\/BIBM.2016.7822672"},{"issue":"1","key":"26_CR5","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s13042-020-01160-0","volume":"12","author":"S Liu","year":"2020","unstructured":"Liu, S., Nie, W., Gao, D., Yang, H., Yan, J., Hao, T.: Clinical quantitative information recognition and entity-quantity association from Chinese electronic medical records. Int. J. Mach. Learn. Cybern. 12(1), 117\u2013130 (2020). https:\/\/doi.org\/10.1007\/s13042-020-01160-0","journal-title":"Int. J. Mach. Learn. Cybern."},{"unstructured":"Hao, T., Wang, H.: Semantic annotation framework (SemAF)\u2014Part 11: Measurable Quantitative Information (MQI). ISO\/DIS 24617-11, International Organization for Standardization (2021)","key":"26_CR6"},{"issue":"1","key":"26_CR7","first-page":"1","volume":"2","author":"KF Wong","year":"2009","unstructured":"Wong, K.F., Li, W.J., Xu, R.F., Zhang, Z.S.: Introduction to Chinese natural language processing. Synt. Lect. Hum. Lang. Technol. 2(1), 1\u2013148 (2009)","journal-title":"Synt. Lect. Hum. Lang. Technol."},{"doi-asserted-by":"crossref","unstructured":"Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. arXiv preprint arXiv:1706.05075 (2017)","key":"26_CR8","DOI":"10.18653\/v1\/P17-1113"},{"doi-asserted-by":"crossref","unstructured":"Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52rd Annual Meeting of the Association for Computational Linguistics, pp. 402\u2013412 (2014)","key":"26_CR9","DOI":"10.3115\/v1\/P14-1038"},{"issue":"03","key":"26_CR10","doi-asserted-by":"publisher","first-page":"266","DOI":"10.3414\/ME15-01-0112","volume":"55","author":"T Hao","year":"2016","unstructured":"Hao, T., Liu, H., Weng, C.: Valx: a system for extracting and structuring numeric lab test comparison statements from text. Methods Inf. Med. 55(03), 266\u2013275 (2016)","journal-title":"Methods Inf. Med."},{"key":"26_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-01078-2_9","volume-title":"Health Information Science","author":"S Liu","year":"2018","unstructured":"Liu, S., Pan, X., Chen, B., Gao, D., Hao, T.: An automated approach for clinical quantitative information extraction from Chinese electronic medical records. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds.) HIS 2018. LNCS, vol. 11148, pp. 98\u2013109. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01078-2_9"},{"issue":"1","key":"26_CR12","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s12911-018-0603-0","volume":"18","author":"T Hao","year":"2018","unstructured":"Hao, T., Pan, X., Gu, Z., Qu, Y., Weng, H.: A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts. BMC Med. Inform. Decis. Mak. 18(1), 15\u201325 (2018)","journal-title":"BMC Med. Inform. Decis. Mak."},{"doi-asserted-by":"crossref","unstructured":"Tang, B., Cao, H., Wu, Y., Jiang, M., Xu, H.: Clinical entity recognition using structural support vector machines with rich features. In: Proceedings of the ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics, pp. 13\u201320 (2012)","key":"26_CR13","DOI":"10.1145\/2390068.2390073"},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.dss.2018.07.004","volume":"113","author":"R Gruss","year":"2018","unstructured":"Gruss, R., Abrahams, A.S., Fan, W., Wang, G.A.: By the numbers: the magic of numerical intelligence in text analytic systems. Decis. Support Syst. 113, 86\u201398 (2018)","journal-title":"Decis. Support Syst."},{"issue":"5","key":"26_CR15","first-page":"1","volume":"19","author":"L Li","year":"2019","unstructured":"Li, L., Zhao, J., Hou, L., Zhai, Y., Shi, J., Cui, F.: An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records. BMC Med. Inform. Decis. Mak. 19(5), 1\u201311 (2019)","journal-title":"BMC Med. Inform. Decis. Mak."},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1554\u20131564 (2018)","key":"26_CR16","DOI":"10.18653\/v1\/P18-1144"},{"issue":"10","key":"26_CR17","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"X Qiu","year":"2020","unstructured":"Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., Huang, X.: Pre-trained models for natural language processing: a survey. Science China Technol. Sci. 63(10), 1872\u20131897 (2020). https:\/\/doi.org\/10.1007\/s11431-020-1647-3","journal-title":"Science China Technol. Sci."},{"key":"26_CR18","doi-asserted-by":"publisher","first-page":"103985","DOI":"10.1016\/j.ijmedinf.2019.103985","volume":"132","author":"X Zhang","year":"2019","unstructured":"Zhang, X., et al.: Extracting comprehensive clinical information for breast cancer using deep learning methods. Int. J. Med. Inform. 132, 103985 (2019)","journal-title":"Int. J. Med. Inform."},{"doi-asserted-by":"crossref","unstructured":"Liu, W., Fu, X., Zhang, Y., Xiao, W.: Lexicon enhanced Chinese sequence labeling using BERT adapter. 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. 5847\u20135858 (2021)","key":"26_CR19","DOI":"10.18653\/v1\/2021.acl-long.454"},{"doi-asserted-by":"crossref","unstructured":"Gui, T., Ma, R., Zhang, Q., Zhao, L., Jiang, Y.G., Huang, X.: CNN-based Chinese NER with lexicon rethinking. In: IJCAI, pp. 4982\u20134988 (2019)","key":"26_CR20","DOI":"10.24963\/ijcai.2019\/692"},{"doi-asserted-by":"crossref","unstructured":"Gui, T., et al.: A lexicon-based graph neural network for Chinese NER. 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. 1040\u20131050 (2019)","key":"26_CR21","DOI":"10.18653\/v1\/D19-1096"},{"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":"26_CR22"},{"doi-asserted-by":"crossref","unstructured":"Lee, K., He, L., Lewis, M., Zettlemoyer, L.: End-to-end neural coreference resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 188\u2013197 (2017)","key":"26_CR23","DOI":"10.18653\/v1\/D17-1018"},{"key":"26_CR24","first-page":"2006","volume":"2020","author":"M Eberts","year":"2020","unstructured":"Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. In ECAI 2020, 2006\u20132013 (2020)","journal-title":"In ECAI"},{"unstructured":"Sui, D., Chen, Y., Liu, K., Zhao, J., Zeng, X., Liu, S.: Joint entity and relation extraction with set prediction networks. In: AAAI (2021)","key":"26_CR25"},{"unstructured":"Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML, vol. 3(2), pp. 282\u2013289 (2001).","key":"26_CR26"},{"doi-asserted-by":"crossref","unstructured":"Chen, X., Qiu, X., Zhu, C., Liu, P., Huang, X.J.: Long short-term memory neural networks for Chinese word segmentation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1197\u20131206 (2015)","key":"26_CR27","DOI":"10.18653\/v1\/D15-1141"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-6135-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T14:09:55Z","timestamp":1666274995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-6135-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811961342","9789811961359"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-6135-9_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2022\/","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":"205","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":"77","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":"0","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":"38% - 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":"3.09","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":"3.68","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}