{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:40:38Z","timestamp":1742913638249,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811619632"},{"type":"electronic","value":"9789811619649"}],"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\/978-981-16-1964-9_4","type":"book-chapter","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T21:06:58Z","timestamp":1620248818000},"page":"41-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Label Enhanced Named Entity Recognition with Incompletely Annotated Data"],"prefix":"10.1007","author":[{"given":"Yunke","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Long","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zhanfeng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"4_CR1","unstructured":"Kumar, S.: A survey of deep learning methods for relation extraction. arXiv preprint arXiv:1705.03645 (2017)"},{"key":"4_CR2","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":"4_CR3","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"key":"4_CR4","unstructured":"Yadav, V., Bethard, S.: A survey on recent advances in named entity recognition from deep learning models. arXiv preprint arXiv:1910.11470 (2019)"},{"key":"4_CR5","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"4_CR6","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"4_CR7","unstructured":"Yan, H., Deng, B., Li, X., Qiu, X.: TENER: adapting transformer encoder for name entity recognition. arXiv preprint arXiv:1911.04474 (2019)"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Akbik, A., Bergmann, T., Vollgraf, R.: Pooled contextualized embeddings for named entity recognition. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (Long and Short Papers), vol. 1, pp. 724\u2013728 (2019)","DOI":"10.18653\/v1\/N19-1078"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Jie, Z., Xie, P., Lu, W., Ding, R., Li, L.: 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, (Long and Short Papers), vol. 1, pp. 729\u2013734 (2019)","DOI":"10.18653\/v1\/N19-1079"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Yang, P., Liu, W., Yang, J.: Positive unlabeled learning via wrapper-based adaptive sampling. In: IJCAI, pp. 3273\u20133279 (2017)","DOI":"10.24963\/ijcai.2017\/457"},{"key":"4_CR11","unstructured":"Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)"},{"key":"4_CR12","unstructured":"Bellare, K., McCallum, A.: Learning extractors from unlabeled text using relevant databases. In Sixth International Workshop on Information Integration on the Web (2007)"},{"key":"4_CR13","unstructured":"Carlson, A., Gaffney, S., Vasile, F.: Learning a named entity tagger from gazetteers with the partial perceptron. In: AAAI Spring Symposium: Learning by Reading and Learning to Read, pp. 7\u201313 (2009)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Greenberg, N., Bansal, T., Verga, P., McCallum, A.: Marginal likelihood training of BILSTM-CRF for biomedical named entity recognition from disjoint label sets. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2824\u20132829 (2018)","DOI":"10.18653\/v1\/D18-1306"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Mayhew, S., Chaturvedi, S., Tsai, C.T., Roth, D.: Named entity recognition with partially annotated training data. arXiv preprint arXiv:1909.09270 (2019)","DOI":"10.18653\/v1\/K19-1060"},{"key":"4_CR16","unstructured":"Chang, M.W., Ratinov, L., Roth, D.: Guiding semi-supervision with constraint-driven learning. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 280\u2013287 (2007)"},{"key":"4_CR17","unstructured":"Lee, W.S., Liu, B.: Learning with positive and unlabeled examples using weighted logistic regression. In: ICML, vol. 3, pp. 448\u2013455 (2003)"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Clark, K., Luong, M.T., Manning, C.D., Le, Q.V.: Semi-supervised sequence modeling with cross-view training. arXiv preprint arXiv:1809.08370 (2018)","DOI":"10.18653\/v1\/D18-1217"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Cao, Y., Hu, Z., Chua, T.S., Liu, Z., Ji, H.: Low-resource name tagging learned with weakly labeled data. arXiv preprint arXiv:1908.09659 (2019)","DOI":"10.18653\/v1\/D19-1025"},{"key":"4_CR20","unstructured":"Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially supervised classification of text documents. In: ICML, vol. 2, pp. 387\u2013394. Citeseer (2002)"},{"key":"4_CR21","unstructured":"Liu, B., Dai, Y., Li, X., Lee, W.S., Yu, P.S.: Building text classifiers using positive and unlabeled examples. In: Third IEEE International Conference on Data Mining, pp. 179\u2013186. IEEE (2003)"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 213\u2013220 (2008)","DOI":"10.1145\/1401890.1401920"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Peng, M., Xing, X., Zhang, Q., Fu, J., Huang, X.: Distantly supervised named entity recognition using positive-unlabeled learning. arXiv preprint arXiv:1906.01378 (2019)","DOI":"10.18653\/v1\/P19-1231"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009), (2009)","DOI":"10.3115\/1596374.1596399"},{"issue":"8","key":"4_CR25","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"4_CR26","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"key":"4_CR27","unstructured":"Qiao, Y., Xiong, C., Liu, Z., Liu, Z.: Understanding the behaviors of BERT in ranking. arXiv preprint arXiv:1904.07531 (2019)"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Wei, J., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196 (2019)","DOI":"10.18653\/v1\/D19-1670"},{"key":"4_CR29","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"4_CR30","unstructured":"Jiao, Z., Sun, S., Sun, K.: Chinese lexical analysis with deep Bi-GRU-CRF network. arXiv preprint arXiv:1807.01882 (2018)"},{"key":"4_CR31","unstructured":"Cui, Y., et al.: Pre-training with whole word masking for Chinese BERT. arXiv preprint arXiv:1906.08101 (2019)"}],"container-title":["Communications in Computer and Information Science","Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-1964-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T21:08:09Z","timestamp":1620248889000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1964-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811619632","9789811619649"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1964-9_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCKS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Conference on Knowledge Graph and Semantic Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanchang","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccks2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sigkg.cn\/ccks2020\/","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":"173","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":"26","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":"15% - 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":"5.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}