{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T15:50:27Z","timestamp":1749657027007,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031067938"},{"type":"electronic","value":"9783031067945"}],"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\/978-3-031-06794-5_46","type":"book-chapter","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T23:03:27Z","timestamp":1656889407000},"page":"573-582","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey of Deep Learning for Named Entity Recognition in Chinese Social Media"],"prefix":"10.1007","author":[{"given":"Jingxin","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jieren","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Ziyan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Congqiang","family":"Lou","sequence":"additional","affiliation":[]},{"given":"Chenli","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"46_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1075\/li.30.1.03nad","volume":"30","author":"D Nadeau","year":"2007","unstructured":"Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30, 3\u201326 (2007)","journal-title":"Lingvisticae Investigationes"},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Tran, P., Ta, V., Truong, Q., Duong, Q., Nguyen, T., Phan, X.: Named entity recognition for vietnamese spoken texts and its application in smart mobile voice interaction. In: Nguyen, N.T., Trawi\u0144ski, B., Fujita, H., Hong, TP. (eds.) Intelligent Information and Database Systems. ACIIDS 2016. LNCS, vol. 9621, pp. 170\u2013180. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49381-6_17","DOI":"10.1007\/978-3-662-49381-6_17"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhang, Y., Dong, F.: Neural reranking for named entity recognition RANLP. In: Advances in Natural Language Processing Meet Deep Learning, pp. 84\u201392 (2017)","DOI":"10.26615\/978-954-452-049-6_101"},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Ma, Z., Gao, L., Xu, Y., Sun, T.: Application of pre-training models in named entity recognition. In: 2020 12th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 1, pp. 23\u201326. IEEE (2020)","DOI":"10.1109\/IHMSC49165.2020.00013"},{"key":"46_CR5","unstructured":"Klinger, R., Friedrich, C.: User\u2019s choice of precision and. named entity recognition. In: Proceedings of the International Conference RANLP-2009, pp. 92\u201396 (2009)"},{"issue":"3","key":"46_CR6","first-page":"2807","volume":"67","author":"S Yoo","year":"2021","unstructured":"Yoo, S., Jeong, O.: EP-Bot: empathetic chatbot using auto-growing knowledge graph. Comput. Mater. Cont. 67(3), 2807\u20132817 (2021)","journal-title":"Comput. Mater. Cont."},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"He, Q., Wu, L., Yin, Cai, Y., H: Knowledge-graph augmented word representations for named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 19\u201326 (2020)","DOI":"10.1609\/aaai.v34i05.6299"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Lossio-Ventura, J., et al.: Towards an obesity-cancer knowledge base: Biomedical entity identification and relation detection. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 81\u201388. IEEE (2016)","DOI":"10.1109\/BIBM.2016.7822672"},{"key":"46_CR9","unstructured":"Loster, M. Knowledge base construction with machine learning methods. Universit\u00e4t Potsdam (2021)"},{"key":"46_CR10","unstructured":"He, Z., Li, W.H.: Named entity recognition and disambiguation. General Information (2013)"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Adak, C., Chaudhuri, B., Blumenstein, M.: Named entity recognition from unstructured handwritten document. In: Images Document Analysis Systems, pp. 75\u201380. IEEE (2016)","DOI":"10.1109\/DAS.2016.15"},{"key":"46_CR12","first-page":"495","volume":"20","author":"S Dandapat","year":"2016","unstructured":"Dandapat, S., Way, A.: Improved named entity recognition using machine translation- based cross-lingual. Information Computacion Y Sistemas 20, 495\u2013504 (2016)","journal-title":"Information Computacion Y Sistemas"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"Li, Z., Qu, D., Xie, C., Li, Y.: Language model pre-training method in machine translation based on named entity recognition. Int. J. Artif. Intell. Tools 29(7n08), 2040021 (2020)","DOI":"10.1142\/S0218213020400217"},{"issue":"3","key":"46_CR14","first-page":"4763","volume":"70","author":"A Al-Besher","year":"2022","unstructured":"Al-Besher, A., Kumar, K., Sangeetha, M., Butsa, T.: Bert for conversational question answer- ing systems using semantic similarity estimation. Comput. Mater. Cont. 70(3), 4763\u20134780 (2022)","journal-title":"Comput. Mater. Cont."},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Guan, H.: 2020 Research on named entity recognition of doctor-patient question answering community based on bilstm-crf model. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 41\u201344. IEEE (2020)","DOI":"10.1109\/BIBM49941.2020.9313535"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Lamurias, A., Couto, F.: Biomedical question answering using bidirectional transformers and named entity recognition. In: Proceedings of the 18th BioNLP Workshop and Shared Task, pp. 23\u201327 (2019)","DOI":"10.18653\/v1\/W19-5057"},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"Rau, L.: Extracting company names from text. In: Proceedings of the Seventh IEEE Conference on Artificial Intelligence Application, pp. 29\u201332. IEEE (1991)","DOI":"10.1109\/CAIA.1991.120841"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Bikel, D., Schwarta, R., Weischedel, R.: An algorithm that learns what\u2019s in a. name. Mach. Learn. 34, 211\u2013242 (1999)","DOI":"10.1023\/A:1007558221122"},{"key":"46_CR19","unstructured":"Chinchor, N., Robinson, P.: MUC-7 named entity task definition. In: Proceedings of the 7th Conference on Message Understanding, vol. 29, pp. 1\u201321 (1997)"},{"key":"46_CR20","unstructured":"Wu, Y., Lin, Y.J., Q: Description of the NCU Chinese word segmentation and named entity recognition system for SIGHAN Bakeoff. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pp. 209\u2013221 (2006)"},{"key":"46_CR21","doi-asserted-by":"crossref","unstructured":"Sang, E.F.T.K., DeMeulder, 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":"46_CR22","unstructured":"Mao, X., Dong, Y., He, S., Wang, H., Bao, S.: Chinese word segmentation and named entity recognition based on conditional random fields. In: Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing, pp. 90\u201393 (2008)"},{"key":"46_CR23","unstructured":"Li, L., Mao, T., Huang, D., Yang, Y.: Hybrid models for Chinese named entity recognition. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pp. 72\u201378 (2006)"},{"key":"46_CR24","unstructured":"Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp. 59\u201367 (2011)"},{"key":"46_CR25","unstructured":"Ling, W., Xiang, G., Dyer, C., Alan, B., Isabel, T.: Microblogs as parallel corpora. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 76\u201386 (2013)"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Peng, N., Dredze, M.: Named entity recognition for Chinese social media with jointly trained embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 48\u201354 (2015)","DOI":"10.18653\/v1\/D15-1064"},{"issue":"12","key":"46_CR27","first-page":"4625","volume":"14","author":"J Cheng","year":"2020","unstructured":"Cheng, J., Yang, Y., Tang, X., Xiong, N., Zhang, Y., Lei, F.: Generative adversarial net- works: a literature review. KSII Trans. Internet Inf. Syst. 14(12), 4625\u20134647 (2020)","journal-title":"KSII Trans. Internet Inf. Syst."},{"issue":"13","key":"46_CR28","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.3390\/electronics10131525","volume":"10","author":"F Lei","year":"2021","unstructured":"Lei, F., Cheng, J., Yang, Y., Tang, X., Sheng, V., Huang, C.: Improving heterogeneous network knowledge transfer based on the principle of generative adversarial. Electronics 10(13), 1525 (2021)","journal-title":"Electronics"},{"key":"46_CR29","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.ins.2021.02.004","volume":"565","author":"X Tang","year":"2021","unstructured":"Tang, X., Tu, W., Li, K., Cheng, J.: DFFNet: an IoT-perceptive dual feature fusion network for general real-time semantic segmentation. Inf. Sci. 565, 326\u2013343 (2021)","journal-title":"Inf. Sci."},{"key":"46_CR30","doi-asserted-by":"crossref","unstructured":"Cheng, J., Peng, X., Tang, W., Tu, W., Xu: MIFNet: a lightweight multiscale information fusion network. Int. J. Intell. Syst. 1\u201326 (2021)","DOI":"10.1002\/int.22804"},{"issue":"1","key":"46_CR31","first-page":"407","volume":"66","author":"T Li","year":"2021","unstructured":"Li, T., Hu, Y., Ju, A., Hu, Z.: Adversarial active learning for named entity recognition in cybersecurity. Comput. Mater. Cont. 66(1), 407\u2013420 (2021)","journal-title":"Comput. Mater. Cont."},{"key":"46_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3114378","author":"S Zhao","year":"2021","unstructured":"Zhao, S., Hu, M., Cai, Z., Zhang, Z., Zhou, T., Liu, F.: Enhancing Chinese character representation with lattice-aligned attention. IEEE Trans. Neural Netw. Learn. Syst. (2021). https:\/\/doi.org\/10.1109\/TNNLS.2021.3114378","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"46_CR33","first-page":"2012","volume":"15","author":"J Cheng","year":"2021","unstructured":"Cheng, J., Liu, J., Xu, X.: A review of Chinese named entity recognition. KSII Trans. Internet Inf. Syst. (TIIS) 15(6), 2012\u20132030 (2021)","journal-title":"KSII Trans. Internet Inf. Syst. (TIIS)"},{"key":"46_CR34","unstructured":"He, J., Wang, H.: Chinese named entity recognition and word segmentation based on character. In: Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing, pp. 28\u201332 (2008)"},{"key":"46_CR35","unstructured":"He, Z., Li, W.H., S: The task 2 of CIPS-SIGHAN 2012 named entity recognition and disambiguation. In. Chinese Bakeoff Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing, pp. 108\u2013122 (2012)"},{"key":"46_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981314","author":"J Li","year":"2020","unstructured":"Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. (2020). https:\/\/doi.org\/10.1109\/TKDE.2020.2981314","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"46_CR37","doi-asserted-by":"crossref","unstructured":"Peng, N., Dredze, M.: Improving named entity recognition for Chinese social media with word segmentation representation learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 49\u201355 (2016)","DOI":"10.18653\/v1\/P16-2025"},{"key":"46_CR38","doi-asserted-by":"crossref","unstructured":"He, H., Sun, X.: Score driven max margin neural network for named entity recognition in Chinese social media. In: Proceedings of the 15th Conference of the European, chap. 2, pp. 713\u2013731 (2017)","DOI":"10.18653\/v1\/E17-2113"},{"key":"46_CR39","doi-asserted-by":"crossref","unstructured":"He, H., Sun, X.: A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"46_CR40","doi-asserted-by":"crossref","unstructured":"Wang, B., Chai, Y., Xing, S.: Attention-based recurrent neural model for named entity recognition in. Chinese social media. In: Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, pp. 91\u201396 (2019)","DOI":"10.1145\/3377713.3377771"},{"key":"46_CR41","doi-asserted-by":"crossref","unstructured":"Nie, Y., Tian, Y., Wan, X., Song, Y., Dai, B.: Named entity recognition for social media texts with semantic augmentation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 83\u201391 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.107"},{"key":"46_CR42","unstructured":"Gong, Z., Chen, P., Zhou, J.: integrating boundary assembling into a DNN framework for named entity recognition in Chinese social media text. arXiv:2002.11910 (2020)"},{"key":"46_CR43","doi-asserted-by":"crossref","unstructured":"Dong, C., Wu, H., Zhang, J., Zong, C.: Multichannel LSTM-CRF for named entity recognition in Chinese social media. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds.) NLP-NABD CCL 2017. LNCS, vol. 10565, pp. 197\u2013208. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-69005-6_17","DOI":"10.1007\/978-3-319-69005-6_17"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06794-5_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T10:42:31Z","timestamp":1727520151000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06794-5_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031067938","9783031067945"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06794-5_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Adaptive and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qinghai","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":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icais2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icaisconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1124","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":"116","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":"52","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":"10% - 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":"8","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)"}}]}}