{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:39:37Z","timestamp":1743111577814,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030893620"},{"type":"electronic","value":"9783030893637"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-89363-7_2","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"17-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Weak Supervision Approach with Adversarial Training for Named Entity Recognition"],"prefix":"10.1007","author":[{"given":"Jianxuan","family":"Shao","sequence":"first","affiliation":[]},{"given":"Chenyang","family":"Bu","sequence":"additional","affiliation":[]},{"given":"Shengwei","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., et al.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"key":"2_CR2","unstructured":"Maxwell, J.C.: A Treatise on Electricity and Magnetism, 3rd edn, vol. 2, pp. 68\u201373. Clarendon, Oxford (1892)"},{"key":"2_CR3","unstructured":"Devlin, J., Chang, M.W., Lee, K., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"2_CR4","unstructured":"Yan, H., Deng, B., Li, X., et al.: Tener: Adapting transformer encoder for named entity recognition. arXiv preprint arXiv:1911.04474 (2019)"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JPC Chiu","year":"2016","unstructured":"Chiu, J.P.C., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357\u2013370 (2016)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Li, J., Sun, A., Han, J., et al.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. (2020)","DOI":"10.1109\/TKDE.2020.2981314"},{"key":"2_CR7","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Strubell, E., Verga, P., Belanger, D., et al.: Fast and accurate entity recognition with iterated dilated convolutions. arXiv preprint arXiv:1702.02098 (2017)","DOI":"10.18653\/v1\/D17-1283"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Lin, B.Y., Xu, F.F., Luo, Z., et al.: Multi-channel BiLSTM-CRF model for emerging named entity recognition in social media. In: Proceedings of the 3rd Workshop on Noisy User-generated Text, pp. 160\u2013165 (2017)","DOI":"10.18653\/v1\/W17-4421"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Li, J., Bu, C., Li, P., et al.: A coarse-to-fine collective entity linking method for heterogeneous information networks. Knowl.-Based Syst. 228(2), 107286 (2021)","DOI":"10.1016\/j.knosys.2021.107286"},{"key":"2_CR11","unstructured":"The website of HanLP. https:\/\/hanlp.hankcs.com\/docs\/references.html. Accessed 21 May 2020"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Geng, Z., Yan, H., Qiu, X., et al.: fastHan: A BERT-based Joint Many-Task Toolkit for Chinese NLP. arXiv preprint arXiv:2009.08633 (2020)","DOI":"10.18653\/v1\/2021.acl-demo.12"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., et al.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003\u20131011 (2009)","DOI":"10.3115\/1690219.1690287"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Wu, G., Bu, C., et al.: Chinese entity relation extraction based on syntactic features. In: 2018 IEEE International Conference on Big Knowledge (ICBK). IEEE (2018)","DOI":"10.1109\/ICBK.2018.00021"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Shen, Y., Yun, H., Lipton, Z.C., et al.: Deep active learning for named entity recognition. arXiv preprint arXiv:1707.05928 (2017)","DOI":"10.18653\/v1\/W17-2630"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Shang, J., Liu, L., Ren, X., et al.: Learning named entity tagger using domain-specific dictionary. arXiv preprint arXiv:1809.03599 (2018)","DOI":"10.18653\/v1\/D18-1230"},{"key":"2_CR17","unstructured":"Fries, J., Wu, S., Ratner, A., et al.: Swellshark: A generative model for biomedical named entity recognition without labeled data. arXiv preprint arXiv:1704.06360 (2017)"},{"key":"2_CR18","unstructured":"Yang, Y., Chen, W., Li, Z., et al.: Distantly supervised NER with partial annotation learning and reinforcement learning. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2159\u20132169 (2018)"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Jie, Z., Xie, P., Lu, W., et al.: Better modeling of incomplete annotations for named entity recognition. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 729\u2013734 (2019)","DOI":"10.18653\/v1\/N19-1079"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Liu, A., Du, J., Stoyanov, V.: Knowledge-augmented language model and its application to unsupervised named-entity recognition. arXiv preprint arXiv:1904.04458 (2019)","DOI":"10.18653\/v1\/N19-1117"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Lison, P., Hubin, A., Barnes, J., et al.: Named entity recognition without labelled data: A weak supervision approach. arXiv preprint arXiv:2004.14723 (2020)","DOI":"10.18653\/v1\/2020.acl-main.139"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhao, H., Zhan, J.: Named Entity Recognition Only from Word Embeddings. arXiv preprint arXiv:1909.00164 (2019)","DOI":"10.18653\/v1\/2020.emnlp-main.723"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Safranchik, E., Luo, S., Bach, S.: Weakly supervised sequence tagging from noisy rules. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no.04, pp. 5570\u20135578 (2020)","DOI":"10.1609\/aaai.v34i04.6009"},{"key":"2_CR24","unstructured":"Madry, A., Makelov, A., Schmidt, L., et al.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"2_CR25","unstructured":"Miyato, T., Dai, A.M., Goodfellow, I.: Adversarial training methods for semi-supervised text classification. arXiv preprint arXiv:1605.07725 (2016)"},{"key":"2_CR26","unstructured":"Levow, G.A.: The third international Chinese language processing bakeoff: word segmentation and named entity recognition. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pp. 108\u2013117 (2006)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. arXiv preprint arXiv:1805.02023 (2018)","DOI":"10.18653\/v1\/P18-1144"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Ratner, A., Bach, S.H., Ehrenberg, H., et al.: Snorkel: rapid training data creation with weak supervision. Proc. VLDB Endow. 11(3) (2017)","DOI":"10.14778\/3157794.3157797"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Li, X., Yan, H., Qiu, X., et al.: FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.611"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89363-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:16:11Z","timestamp":1635729371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89363-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893620","9783030893637"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89363-7_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","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":"382","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":"93","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":"28","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":"5","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)"}}]}}