{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:24:03Z","timestamp":1775744643662,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031446955","type":"print"},{"value":"9783031446962","type":"electronic"}],"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\/978-3-031-44696-2_47","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T09:03:59Z","timestamp":1696669439000},"page":"601-613","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["KESDT: Knowledge Enhanced Shallow and\u00a0Deep Transformer for\u00a0Detecting Adverse Drug Reactions"],"prefix":"10.1007","author":[{"given":"Yunzhi","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongxuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"issue":"5","key":"47_CR1","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1111\/j.1365-2125.1994.tb05705.x","volume":"37","author":"N Baber","year":"1994","unstructured":"Baber, N.: International conference on harmonisation of technical requirements for registration of pharmaceuticals for human use (ICH). Br. J. Clin. Pharmacol. 37(5), 401 (1994)","journal-title":"Br. J. Clin. Pharmacol."},{"key":"47_CR2","doi-asserted-by":"crossref","unstructured":"Kanchan, S., Gaidhane, A.: Social media role and its impact on public health: a narrative review. Cureus 15(1) (2023)","DOI":"10.7759\/cureus.33737"},{"key":"47_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103896","volume":"123","author":"T Zhang","year":"2021","unstructured":"Zhang, T., Lin, H., Xu, B., Yang, L., Wang, J., Duan, X.: Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions. J. Biomed. Inform. 123, 103896 (2021)","journal-title":"J. Biomed. Inform."},{"key":"47_CR4","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.jbi.2014.11.002","volume":"53","author":"A Sarker","year":"2015","unstructured":"Sarker, A., Gonzalez, G.: Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J. Biomed. Inform. 53, 196\u2013207 (2015)","journal-title":"J. Biomed. Inform."},{"key":"47_CR5","doi-asserted-by":"crossref","unstructured":"Yadav, S., Ekbal, A., Saha, S., Bhattacharyya, P.: A unified multi-task adversarial learning framework for pharmacovigilance mining. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5234\u20135245 (2019)","DOI":"10.18653\/v1\/P19-1516"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Chowdhury, S., Zhang, C., Yu, P.S.: Multi-task pharmacovigilance mining from social media posts. In: Proceedings of the 2018 World Wide Web Conference, pp. 117\u2013126 (2018)","DOI":"10.1145\/3178876.3186053"},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Huang, J.Y., Lee, W.P., Lee, K.D.: Predicting adverse drug reactions from social media posts: data balance, feature selection and deep learning. In: Healthcare, vol. 10, p. 618. MDPI (2022)","DOI":"10.3390\/healthcare10040618"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"47_CR9","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1177\/0165551521991022","volume":"49","author":"NR Aljohani","year":"2023","unstructured":"Aljohani, N.R., Fayoumi, A., Hassan, S.U.: A novel focal-loss and class-weight-aware convolutional neural network for the classification of in-text citations. J. Inf. Sci. 49(1), 79\u201392 (2023)","journal-title":"J. Inf. Sci."},{"issue":"1","key":"47_CR10","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/msb.2009.98","volume":"6","author":"M Kuhn","year":"2010","unstructured":"Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(1), 343 (2010)","journal-title":"Mol. Syst. Biol."},{"key":"47_CR11","doi-asserted-by":"crossref","unstructured":"Benton, A., et al.: Identifying potential adverse effects using the web: a new approach to medical hypothesis generation. J. Biomed. Inform. 44(6), 989\u2013996 (2011)","DOI":"10.1016\/j.jbi.2011.07.005"},{"key":"47_CR12","doi-asserted-by":"publisher","unstructured":"Yates, A., Goharian, N.: ADRTrace: detecting expected and unexpected adverse drug reactions from user reviews on social media sites. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 816\u2013819. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-36973-5_92","DOI":"10.1007\/978-3-642-36973-5_92"},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Bian, J., Topaloglu, U., Yu, F.: Towards large-scale twitter mining for drug-related adverse events. In: Proceedings of the 2012 International Workshop on Smart Health and Wellbeing, pp. 25\u201332 (2012)","DOI":"10.1145\/2389707.2389713"},{"key":"47_CR14","unstructured":"Patki, A., et al.: Mining adverse drug reaction signals from social media: going beyond extraction. Proc. BioLinkSig 2014, 1\u20138 (2014)"},{"key":"47_CR15","unstructured":"Rastegar-Mojarad, M., Elayavilli, R.K., Yu, Y., Liu, H.: Detecting signals in noisy data-can ensemble classifiers help identify adverse drug reaction in tweets. In: Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing (2016)"},{"key":"47_CR16","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.neucom.2021.01.092","volume":"440","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Lin, H., Yang, L., Xu, B., Diao, Y., Ren, L.: Dual part-pooling attentive networks for session-based recommendation. Neurocomputing 440, 89\u2013100 (2021)","journal-title":"Neurocomputing"},{"key":"47_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Price does matter! modeling price and interest preferences in session-based recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1684\u20131693 (2022)","DOI":"10.1145\/3477495.3532043"},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Dynamic intent-aware iterative denoising network for session-based recommendation. Inf. Process. Manag. 59(3), 102936 (2022)","DOI":"10.1016\/j.ipm.2022.102936"},{"key":"47_CR19","unstructured":"Huynh, T., He, Y., Willis, A., R\u00fcger, S.: Adverse drug reaction classification with deep neural networks. Coling (2016)"},{"key":"47_CR20","doi-asserted-by":"publisher","unstructured":"Alimova, I., Solovyev, V.: Interactive attention network for adverse drug reaction classification. In: Ustalov, D., Filchenkov, A., Pivovarova, L., \u017di\u017eka, J. (eds.) AINL 2018. CCIS, vol. 930, pp. 185\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01204-5_18","DOI":"10.1007\/978-3-030-01204-5_18"},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Liu, J., Wu, S., Huang, Y., Xie, X.: Detecting tweets mentioning drug name and adverse drug reaction with hierarchical tweet representation and multi-head self-attention. In: Proceedings of the 2018 EMNLP Workshop SMM4H: the 3rd Social Media Mining for Health Applications Workshop and Shared Task, pp. 34\u201337 (2018)","DOI":"10.18653\/v1\/W18-5909"},{"key":"47_CR22","doi-asserted-by":"crossref","unstructured":"Raval, S., Sedghamiz, H., Santus, E., Alhanai, T., Ghassemi, M., Chersoni, E.: Exploring a unified sequence-to-sequence transformer for medical product safety monitoring in social media. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3534\u20133546 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.300"},{"key":"47_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103431","volume":"106","author":"Z Li","year":"2020","unstructured":"Li, Z., Yang, Z., Luo, L., Xiang, Y., Lin, H.: Exploiting adversarial transfer learning for adverse drug reaction detection from texts. J. Biomed. Inform. 106, 103431 (2020)","journal-title":"J. Biomed. Inform."},{"key":"47_CR24","doi-asserted-by":"crossref","unstructured":"Wu, L., et al.: Graph neural networks for natural language processing: a survey. Found. Trends\u00ae Mach. Learn. 16(2), 119\u2013328 (2023)","DOI":"10.1561\/2200000096"},{"key":"47_CR25","doi-asserted-by":"publisher","unstructured":"Kwak, H., Lee, M., Yoon, S., Chang, J., Park, S., Jung, K.: Drug-disease graph: predicting adverse drug reaction signals via graph neural network with clinical data. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 633\u2013644. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_48","DOI":"10.1007\/978-3-030-47436-2_48"},{"key":"47_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107324","volume":"106","author":"C Shen","year":"2021","unstructured":"Shen, C., Li, Z., Chu, Y., Zhao, Z.: Gar: graph adversarial representation for adverse drug event detection on twitter. Appl. Soft Comput. 106, 107324 (2021)","journal-title":"Appl. Soft Comput."},{"key":"47_CR27","doi-asserted-by":"publisher","unstructured":"Gao, Y., Ji, S., Zhang, T., Tiwari, P., Marttinen, P.: Contextualized graph embeddings for adverse drug event detection. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, 19\u201323 September 2022, Proceedings, Part II, pp. 605\u2013620. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-26390-3_35","DOI":"10.1007\/978-3-031-26390-3_35"},{"key":"47_CR28","first-page":"65","volume":"23","author":"P Mozzicato","year":"2009","unstructured":"Mozzicato, P.: Meddra: an overview of the medical dictionary for regulatory activities. Pharmaceut. Med. 23, 65\u201375 (2009)","journal-title":"Pharmaceut. Med."},{"key":"47_CR29","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)","DOI":"10.18653\/v1\/2021.acl-long.454"},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"Alvaro, N., et al.: Twimed: twitter and pubmed comparable corpus of drugs, diseases, symptoms, and their relations. JMIR Publ. Health Surveill. 3(2), e6396 (2017)","DOI":"10.2196\/publichealth.6396"},{"key":"47_CR31","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jbi.2015.03.010","volume":"55","author":"S Karimi","year":"2015","unstructured":"Karimi, S., Metke-Jimenez, A., Kemp, M., Wang, C.: Cadec: a corpus of adverse drug event annotations. J. Biomed. Inform. 55, 73\u201381 (2015)","journal-title":"J. Biomed. Inform."},{"key":"47_CR32","doi-asserted-by":"crossref","unstructured":"Sarker, A., Nikfarjam, A., Gonzalez, G.: Social media mining shared task workshop. In: Biocomputing 2016: Proceedings of the Pacific Symposium, pp. 581\u2013592. World Scientific (2016)","DOI":"10.1142\/9789814749411_0054"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44696-2_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T09:11:24Z","timestamp":1696669884000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44696-2_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031446955","9783031446962"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44696-2_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Foshan","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2023\/index.php","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":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"478","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":"143","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":"30% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}