{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:24:01Z","timestamp":1743128641289,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030454388"},{"type":"electronic","value":"9783030454395"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-45439-5_24","type":"book-chapter","created":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T04:02:50Z","timestamp":1586577770000},"page":"356-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Mixed Semantic Features Model for Chinese NER with Characters and Words"],"prefix":"10.1007","author":[{"given":"Ning","family":"Chang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Bikel, D.M., Miller, S., Schwartz, R., Weischedel, R.: Nymble: a high-performance learning name-finder. In: Conference on Applied Natural Language Processing (1997)","DOI":"10.3115\/974557.974586"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Cao, P., Chen, Y., Liu, K., Zhao, J., Liu, S.: Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 182\u2013192 (2018)","DOI":"10.18653\/v1\/D18-1017"},{"key":"24_CR3","unstructured":"Chen, A., Peng, F., Shan, R., Sun, G.: Chinese named entity recognition with conditional probabilistic models. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pp. 173\u2013176 (2006)"},{"issue":"Aug","key":"24_CR4","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493\u20132537 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Ding, R., Xie, P., Zhang, X., Lu, W., Li, L., Si, L.: A neural multi-digraph model for Chinese NER with gazetteers. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 1462\u20131467. Association for Computational Linguistics, July 2019. https:\/\/doi.org\/10.18653\/v1\/P19-1141","DOI":"10.18653\/v1\/P19-1141"},{"key":"24_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-3-319-50496-4_20","volume-title":"Natural Language Understanding and Intelligent Applications","author":"C Dong","year":"2016","unstructured":"Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H.: Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL\/NLPCC 2016. LNCS (LNAI), vol. 10102, pp. 239\u2013250. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50496-4_20"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"He, H., Sun, X.: F-score driven max margin neural network for named entity recognition in Chinese social media. arXiv preprint arXiv:1611.04234 (2016)","DOI":"10.18653\/v1\/E17-2113"},{"key":"24_CR8","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: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10977"},{"issue":"8","key":"24_CR9","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":"24_CR10","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv:1508.01991 [cs], August 2015"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Isozaki, H., Kazawa, H.: Efficient support vector classifiers for named entity recognition. In: International Conference on Computational Linguistics, pp. 1\u20137. Association for Computational Linguistics (2002)","DOI":"10.3115\/1072228.1072282"},{"key":"24_CR12","unstructured":"Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. CoRR abs\/1603.01360 (2016). http:\/\/arxiv.org\/abs\/1603.01360","DOI":"10.18653\/v1\/N16-1030"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Liu, T., Yao, J.Q., Lin, C.Y.: Towards improving neural named entity recognition with gazetteers. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5301\u20135307 (2019)","DOI":"10.18653\/v1\/P19-1524"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Ma, X.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)","DOI":"10.18653\/v1\/P16-1101"},{"key":"24_CR16","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. 548\u2013554 (2015)","DOI":"10.18653\/v1\/D15-1064"},{"key":"24_CR17","unstructured":"Peng, N., Dredze, M.: Improving named entity recognition for Chinese social media with word segmentation representation learning. arXiv preprint arXiv:1603.00786, pp. 149\u2013155 (2016). http:\/\/aclweb.org\/anthology\/P16-2025"},{"key":"24_CR18","unstructured":"Rei, M., Crichton, G.K., Pyysalo, S.: Attending to characters in neural sequence labeling models. arXiv preprint arXiv:1611.04361 (2016)"},{"key":"24_CR19","unstructured":"Shao, Y., Hardmeier, C., Tiedemann, J., Nivre, J.: Character-based joint segmentation and POS tagging for Chinese using bidirectional RNN-CRF. arXiv preprint arXiv:1704.01314 (2017)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Shen, Y., Yun, H., Lipton, Z.C., Kronrod, Y., Anandkumar, A.: Deep active learning for named entity recognition. arXiv preprint arXiv:1707.05928 (2017)","DOI":"10.18653\/v1\/W17-2630"},{"key":"24_CR21","unstructured":"Vaswani, A., et al.: Attention Is All You Need. arXiv:1706.03762 [cs], June 2017"},{"key":"24_CR22","unstructured":"Xiang, Y., et al.: Chinese named entity recognition with character-word mixed embedding. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2055\u20132058. ACM (2017)"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Xu, M., Jiang, H., Watcharawittayakul, S.: A local detection approach for named entity recognition and mention detection. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1237\u20131247 (2017)","DOI":"10.18653\/v1\/P17-1114"},{"key":"24_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-99495-6_16","volume-title":"Natural Language Processing and Chinese Computing","author":"F Yang","year":"2018","unstructured":"Yang, F., Zhang, J., Liu, G., Zhou, J., Zhou, C., Sun, H.: Five-stroke based CNN-BiRNN-CRF network for Chinese named entity recognition. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2018. LNCS (LNAI), vol. 11108, pp. 184\u2013195. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99495-6_16"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, M., Chen, W., Zhang, W., Wang, H., Zhang, M.: Adversarial Learning for Chinese NER from Crowd Annotations. arXiv:1801.05147 [cs], January 2018","DOI":"10.1609\/aaai.v32i1.11507"},{"key":"24_CR26","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"},{"issue":"2","key":"24_CR27","first-page":"225","volume":"22","author":"J Zhou","year":"2013","unstructured":"Zhou, J., Qu, W., Zhang, F.: Chinese named entity recognition via joint identification and categorization. Chin. J. Electron. 22(2), 225\u2013230 (2013)","journal-title":"Chin. J. Electron."},{"key":"24_CR28","unstructured":"Zhu, Y., Wang, G., Karlsson, B.F.: CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition. arXiv:1904.02141 [cs], April 2019"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Zukov-Gregoric, A., Bachrach, Y., Minkovsky, P., Coope, S., Maksak, B.: Neural named entity recognition using a self-attention mechanism. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 652\u2013656. IEEE (2017)","DOI":"10.1109\/ICTAI.2017.00104"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45439-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:15:28Z","timestamp":1710357328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45439-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030454388","9783030454395"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45439-5_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"14 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2020.org\/","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":"457","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":"55","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":"46","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":"12% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Also included: 8 reproducibility papers, 10 demonstration papers, 12 CLEF organizers lab track papers, 7 doctoral consortium papers, 4 workshops, 3 tutorials. Due to the COVID-19 pandemic, this conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}