{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T08:45:18Z","timestamp":1743497118355},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"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":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s11432-020-2982-y","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T20:02:46Z","timestamp":1600977766000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hierarchical LSTM with char-subword-word tree-structure representation for Chinese named entity recognition"],"prefix":"10.1007","volume":"63","author":[{"given":"Chen","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingrong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenliang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"2982_CR1","doi-asserted-by":"crossref","unstructured":"Chen Y B, Xu L H, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2015. 167\u2013176","DOI":"10.3115\/v1\/P15-1017"},{"key":"2982_CR2","doi-asserted-by":"crossref","unstructured":"Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2016. 1105\u20131116","DOI":"10.18653\/v1\/P16-1105"},{"key":"2982_CR3","unstructured":"Diefenbach D, Lopez V, Singh K, et al. Core techniques of question answering systems over knowledge bases: a survey. Knowl Inform Syst, 2018. https:\/\/hal.archives-ouvertes.fr\/hal-01637143\/document"},{"key":"2982_CR4","first-page":"2725","volume":"27","author":"J F Yang","year":"2016","unstructured":"Yang J F, Guan Y, He B, et al. Corpus construction for named entities and entity relations on Chinese electronic medical records. J Softw, 2016, 27: 2725\u20132746","journal-title":"J Softw"},{"key":"2982_CR5","doi-asserted-by":"crossref","unstructured":"Song L F, Zhang Y, Gildea D, et al. Leveraging dependency forest for neural medical relation extraction. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019. 208\u2013218","DOI":"10.18653\/v1\/D19-1020"},{"key":"2982_CR6","doi-asserted-by":"crossref","unstructured":"Tian Y H, Ma W C, Xia F, et al. ChiMed: a Chinese medical corpus for question answering. In: Proceedings of the 18th BioNLP Workshop and Shared Task, 2019. 250\u2013260","DOI":"10.18653\/v1\/W19-5027"},{"key":"2982_CR7","doi-asserted-by":"crossref","unstructured":"Saito K, Nagata M. Multi-language named-entity recognition system based on HMM. In: Proceedings of Annual Meeting of the Association for Computational Linguistics Workshop on Multilingual and Mixed-language Named Entity Recognition, 2003","DOI":"10.3115\/1119384.1119390"},{"key":"2982_CR8","first-page":"87","volume":"27","author":"H K Yu","year":"2006","unstructured":"Yu H K, Zhang H P, Liu Q, et al. Chinese named entity identification using cascaded hidden Markov model. J Commun, 2006, 27: 87\u201394","journal-title":"J Commun"},{"key":"2982_CR9","doi-asserted-by":"crossref","unstructured":"Solorio T, L\u00f3pez A. Learning named entity classifiers using support vector machines. In: Proceedings of Conference on Computational Linguistics and Natural Language Processing (CICLing), 2004. 158\u2013167","DOI":"10.1007\/978-3-540-24630-5_19"},{"key":"2982_CR10","doi-asserted-by":"crossref","unstructured":"Mccallum A, Li W. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), 2003. 188\u2013191","DOI":"10.3115\/1119176.1119206"},{"key":"2982_CR11","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"J P C Chiu","year":"2016","unstructured":"Chiu J P C, Nichols E. Named entity recognition with bidirectional LSTM-CNNs. Trans Assoc Comput Linguist, 2016, 4: 357\u2013370","journal-title":"Trans Assoc Comput Linguist"},{"key":"2982_CR12","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition. In: Proceedings of Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), 2016. 260\u2013270","DOI":"10.18653\/v1\/N16-1030"},{"key":"2982_CR13","doi-asserted-by":"crossref","unstructured":"Liu L Y, Shang J B, Xu F, et al. Empower sequence labeling with task-aware neural language model. In: Proceedings of Association for the Advance of Artificial Intelligence (AAAI), 2018. 5253\u20135260","DOI":"10.1609\/aaai.v32i1.12006"},{"key":"2982_CR14","doi-asserted-by":"crossref","unstructured":"Dong C H, Zhang J J, Zong C Q, et al. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Proceedings of International Conference on Computer Processing of Oriental Languages (ICCPOL), 2016. 239\u2013250","DOI":"10.1007\/978-3-319-50496-4_20"},{"key":"2982_CR15","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J. Chinese NER using lattice LSTM. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2018. 1554\u20131564","DOI":"10.18653\/v1\/P18-1144"},{"key":"2982_CR16","doi-asserted-by":"crossref","unstructured":"Gui T, Zou Y C, Zhang Q, et al. A lexicon-based graph neural network for Chinese NER. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019. 1040\u20131050","DOI":"10.18653\/v1\/D19-1096"},{"key":"2982_CR17","doi-asserted-by":"crossref","unstructured":"Liu W, Xu T G, Xu Q H, et al. An encoding strategy based word-character LSTM for Chinese NER. In: Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), 2019. 2379\u20132389","DOI":"10.18653\/v1\/N19-1247"},{"key":"2982_CR18","doi-asserted-by":"crossref","unstructured":"Sui D B, Chen Y B, Liu K, et al. Leverage lexical knowledge for Chinese named entity recognition via collaborative graph network. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019. 3828\u20133838","DOI":"10.18653\/v1\/D19-1396"},{"key":"2982_CR19","doi-asserted-by":"crossref","unstructured":"Gong C, Li Z H, Zhang M, et al. Multi-grained Chinese word segmentation. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2017. 703\u2013714","DOI":"10.18653\/v1\/D17-1072"},{"key":"2982_CR20","unstructured":"Heinzerling B, Strube M. BPEmb: tokenization-free pre-trained subword embeddings in 275 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC), 2018. 2989\u20132993"},{"key":"2982_CR21","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch. J Mach Learn Res, 2011, 12: 2493\u20132537","journal-title":"J Mach Learn Res"},{"key":"2982_CR22","doi-asserted-by":"crossref","unstructured":"He H F, Sun X. A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media. In: Proceedings of Association for the Advance of Artificial Intelligence (AAAI), 2017. 3216\u20133222","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"2982_CR23","unstructured":"Zhao H, Kit C Y. Unsupervised segmentation helps supervised learning of character tagging for word segmentation and namedentity recognition. In: Proceedings of SIGHAN Workshop on Chinese Language Processing, 2008"},{"key":"2982_CR24","doi-asserted-by":"crossref","unstructured":"Peng N Y, Dredze M. Named entity recognition for Chinese social media with jointly trained embeddings. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2015. 548\u2013554","DOI":"10.18653\/v1\/D15-1064"},{"key":"2982_CR25","doi-asserted-by":"crossref","unstructured":"He H F, Sun X. F-score driven max margin neural network for named entity recognition in Chinese social media. In: Proceedings of European Chapter of the Association for Computational Linguistics (EACL), 2017. 713\u2013718","DOI":"10.18653\/v1\/E17-2113"},{"key":"2982_CR26","doi-asserted-by":"crossref","unstructured":"Peng N Y, Dredze M. Improving named entity recognition for Chinese social media with word segmentation representation learning. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2016. 149\u2013155","DOI":"10.18653\/v1\/P16-2025"},{"key":"2982_CR27","doi-asserted-by":"crossref","unstructured":"Cao P F, Chen Y B, Liu K, et al. Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2018. 182\u2013192","DOI":"10.18653\/v1\/D18-1017"},{"key":"2982_CR28","doi-asserted-by":"crossref","unstructured":"Sennrich R, Haddow B, Birch A. Neural machine translation of rare words with subword units. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2016. 1715\u20131725","DOI":"10.18653\/v1\/P16-1162"},{"key":"2982_CR29","unstructured":"Weischedel R, Palmer M, Marcus M, et al. OntoNotes Release 4.0. Philadelphia: Linguistic Data Consortium, 2011"},{"key":"2982_CR30","unstructured":"Levow G A. The third international Chinese language processing backoff: word segmentation and named entity recognition. In: Proceedings of SIGHAN Workshop on Chinese Language Processing, 2006. 108\u2013117"},{"key":"2982_CR31","first-page":"540","volume":"46","author":"E Noreen","year":"1990","unstructured":"Noreen E. Computer-intensive methods for testing hypotheses. Biometrics, 1990, 46: 540\u2013541","journal-title":"Biometrics"},{"key":"2982_CR32","unstructured":"Zhang S X, Qin Y, Wen J, et al. Word segmentation and named entity recognition for SIGHAN bakeoff3. In: Proceedings of SIGHAN Workshop on Chinese Language Processing, 2006. 158\u2013161"},{"key":"2982_CR33","first-page":"225","volume":"22","author":"J S Zhou","year":"2013","unstructured":"Zhou J S, Qu W G, Zhang F. Chinese named entity recognition via joint identification and categorization. Chinese J Electron, 2013, 22: 225\u2013230","journal-title":"Chinese J Electron"},{"key":"2982_CR34","doi-asserted-by":"crossref","unstructured":"Li S, Zhao Z, Hu R F, et al. Analogical reasoning on Chinese morphological and semantic relations. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2018. 138\u2013143","DOI":"10.18653\/v1\/P18-2023"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-2982-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-020-2982-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-2982-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T08:48:36Z","timestamp":1668934116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-020-2982-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,16]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["2982"],"URL":"https:\/\/doi.org\/10.1007\/s11432-020-2982-y","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,16]]},"assertion":[{"value":"22 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"202102"}}