{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:08:54Z","timestamp":1760609334122,"version":"3.37.3"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T00:00:00Z","timestamp":1610409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T00:00:00Z","timestamp":1610409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the Second Level Research Project of China Geological Survey","award":["DD20191008"],"award-info":[{"award-number":["DD20191008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11042-020-10089-z","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T01:55:15Z","timestamp":1610502915000},"page":"19135-19149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Named entity recognition for Chinese marine text with knowledge-based self-attention"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9480-5034","authenticated-orcid":false,"given":"Shufeng","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"key":"10089_CR1","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, pp 2787\u20132795"},{"key":"10089_CR2","doi-asserted-by":"crossref","unstructured":"Bunescu R C, Mooney R J (2005) A shortest path dependency kernel for relation extraction. In: EMNLP, pp 724\u2013731","DOI":"10.3115\/1220575.1220666"},{"key":"10089_CR3","doi-asserted-by":"crossref","unstructured":"Cao P, Chen Y, Liu K, Zhao J, Liu S (2018) 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","DOI":"10.18653\/v1\/D18-1017"},{"issue":"1","key":"10089_CR4","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s41095-016-0073-1","volume":"3","author":"L Chen","year":"2017","unstructured":"Chen L, Yang M (2017) Semi-supervised dictionary learning with label propagation for image classification. Comput Vis Media 3(1):83\u201394","journal-title":"Comput Vis Media"},{"key":"10089_CR5","doi-asserted-by":"crossref","unstructured":"Chen Y, Xu L, Liu K, Zeng D, Zhao J (2015) Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL, pp 167\u2013176","DOI":"10.3115\/v1\/P15-1017"},{"key":"10089_CR6","unstructured":"Chorowski J K, Bahdanau D, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. In: Advances in neural information processing systems, pp 577\u2013585"},{"key":"10089_CR7","unstructured":"Devlin J, Chang M -W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"key":"10089_CR8","doi-asserted-by":"crossref","unstructured":"Ebisu T, Ichise R (2018) Toruse: knowledge graph embedding on a lie group. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11538"},{"key":"10089_CR9","unstructured":"Fader A, Zettlemoyer L, Etzioni O (2013) Paraphrase-driven learning for open question answering. In: ACL, pp 1608\u20131618"},{"key":"10089_CR10","doi-asserted-by":"crossref","unstructured":"Fan D -P, Cheng M -M, Liu J -J, Gao S -H, Hou Q, Borji A (2018) Salient objects in clutter: bringing salient object detection to the foreground. In: Proceedings of the European conference on computer vision (ECCV), pp 186\u2013202","DOI":"10.1007\/978-3-030-01267-0_12"},{"issue":"3","key":"10089_CR11","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1109\/PROC.1973.9030","volume":"61","author":"GD Forney","year":"1973","unstructured":"Forney G D (1973) The viterbi algorithm. Proc IEEE 61(3):268\u2013278","journal-title":"Proc IEEE"},{"key":"10089_CR12","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2019.04.062","volume":"356","author":"K Fu","year":"2019","unstructured":"Fu K, Zhao Q, Gu I Y -H, Yang J (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69\u201382","journal-title":"Neurocomputing"},{"key":"10089_CR13","doi-asserted-by":"crossref","unstructured":"Fu K, Fan D -P, Ji G -P, Zhao Q (2020) Jl-dcf: joint learning and densely-cooperative fusion framework for rgb-d salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3052\u20133062","DOI":"10.1109\/CVPR42600.2020.00312"},{"key":"10089_CR14","doi-asserted-by":"crossref","unstructured":"Greenberg N, Bansal T, Verga P, McCallum A (2018) 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","DOI":"10.18653\/v1\/D18-1306"},{"key":"10089_CR15","doi-asserted-by":"crossref","unstructured":"Guo S, Wang Q, Wang L, Wang B, Guo L (2018) Knowledge graph embedding with iterative guidance from soft rules. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11918"},{"key":"10089_CR16","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.elerap.2018.02.006","volume":"28","author":"S Han","year":"2018","unstructured":"Han S, Hao X, Huang H (2018) An event-extraction approach for business analysis from online chinese news. Electron Commer Res Appl 28:244\u2013260","journal-title":"Electron Commer Res Appl"},{"key":"10089_CR17","doi-asserted-by":"crossref","unstructured":"He H, Sun X (2016) F-score driven max margin neural network for named entity recognition in chinese social media. arXiv:1611.04234","DOI":"10.18653\/v1\/E17-2113"},{"key":"10089_CR18","doi-asserted-by":"crossref","unstructured":"He H, Sun X (2017) A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"10089_CR19","unstructured":"Hochreiter S, Schmidhuber J (1997) Lstm can solve hard long time lag problems. In: Advances in neural information processing systems, pp 473\u2013479"},{"key":"10089_CR20","unstructured":"Huang Z, Xu W, Yu K (2015) Bidirectional lstm-crf models for sequence tagging. arXiv:1508.01991"},{"key":"10089_CR21","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167"},{"key":"10089_CR22","doi-asserted-by":"crossref","unstructured":"Ju M, Miwa M, Ananiadou S (2018) A neural layered model for nested named entity recognition. In: Proceedings of the 2018 conference of the North American Chapter Of The Association For Computational Linguistics: human language technologies, vol 1 (Long Papers), pp 1446\u20131459","DOI":"10.18653\/v1\/N18-1131"},{"key":"10089_CR23","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"10089_CR24","unstructured":"Lafferty J, McCallum A, Pereira F C (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data"},{"key":"10089_CR25","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 260\u2013270","DOI":"10.18653\/v1\/N16-1030"},{"issue":"12","key":"10089_CR26","first-page":"55","volume":"8","author":"D Lee","year":"2017","unstructured":"Lee D, Yu W, Lim H (2017) Bi-directional lstm-cnn-crf for korean named entity recognition system with feature augmentation. J Korea Converg Soc 8(12):55\u201362","journal-title":"J Korea Converg Soc"},{"key":"10089_CR27","unstructured":"Lei Ba J, Kiros J R, Hinton G E (2016) Layer normalization. arXiv:1607.06450"},{"key":"10089_CR28","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"10089_CR29","doi-asserted-by":"crossref","unstructured":"Liu W, Xu T, Xu Q, Song J, Zu Y (2019) An encoding strategy based word-character lstm for Chinese ner. 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 2379\u20132389","DOI":"10.18653\/v1\/N19-1247"},{"key":"10089_CR30","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3623\u20133632","DOI":"10.1109\/CVPR.2019.00374"},{"key":"10089_CR31","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Shen J, Tai Y -W, Crandall D J, Hoi S C (2020) Learning video object segmentation from unlabeled videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8960\u20138970","DOI":"10.1109\/CVPR42600.2020.00898"},{"key":"10089_CR32","unstructured":"McClosky D, Surdeanu M, Manning C D (2011) Event extraction as dependency parsing. In: HLT, pp 1626\u20131635"},{"key":"10089_CR33","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G S, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111\u20133119"},{"key":"10089_CR34","doi-asserted-by":"crossref","unstructured":"Miwa M, Bansal M (2016) End-to-end relation extraction using lstms on sequences and tree structures. In: ACL, pp 1105\u20131116","DOI":"10.18653\/v1\/P16-1105"},{"key":"10089_CR35","unstructured":"Ng A Y (2004) Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the twenty-first international conference on machine learning. ACM, p 78"},{"issue":"2","key":"10089_CR36","doi-asserted-by":"publisher","first-page":"488","DOI":"10.3390\/su10020488","volume":"10","author":"G Park","year":"2018","unstructured":"Park G, Kim H (2018) Low-cost implementation of a named entity recognition system for voice-activated human-appliance interfaces in a smart home. Sustainability 10(2):488","journal-title":"Sustainability"},{"key":"10089_CR37","doi-asserted-by":"crossref","unstructured":"Peng N, Dredze M (2015) 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","DOI":"10.18653\/v1\/D15-1064"},{"key":"10089_CR38","doi-asserted-by":"crossref","unstructured":"Peng N, Dredze M (2016) Improving named entity recognition for chinese social media with word segmentation representation learning. arXiv:1603.00786","DOI":"10.18653\/v1\/P16-2025"},{"key":"10089_CR39","doi-asserted-by":"crossref","unstructured":"Peng D, Wang Y, Liu C, Chen Z (2019) Tl-ner: a transfer learning model for chinese named entity recognition. Information Systems Frontiers 1\u201314","DOI":"10.1007\/s10796-019-09932-y"},{"key":"10089_CR40","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"10089_CR41","doi-asserted-by":"crossref","unstructured":"Singh S, Riedel S, Martin B, Zheng J, McCallum A (2013) Joint inference of entities, relations, and coreference. In: AKBC, pp 1\u20136","DOI":"10.1145\/2509558.2509559"},{"key":"10089_CR42","doi-asserted-by":"crossref","unstructured":"Upadhyay S, Gupta N, Roth D (2018) Joint multilingual supervision for cross-lingual entity linking. In: EMNLP, pp 2486\u20132495","DOI":"10.18653\/v1\/D18-1270"},{"key":"10089_CR43","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"issue":"4","key":"10089_CR44","first-page":"563","volume":"32","author":"E Voorhees","year":"2006","unstructured":"Voorhees E, Harman D K (2006) Trec : experiment and evaluation in information retrieval. J Am Soc Inf Sci Technol 32(4):563\u2013567","journal-title":"J Am Soc Inf Sci Technol"},{"key":"10089_CR45","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v28i1.8870"},{"issue":"12","key":"10089_CR46","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29 (12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10089_CR47","doi-asserted-by":"crossref","unstructured":"Wang W, Lu X, Shen J, Crandall D J, Shao L (2019) Zero-shot video object segmentation via attentive graph neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 9236\u20139245","DOI":"10.1109\/ICCV.2019.00933"},{"key":"10089_CR48","unstructured":"Xiang Y, et al. (2017) Chinese named entity recognition with character-word mixed embedding. In: Proceedings of the 2017 ACM on conference on information and knowledge management. ACM, pp 2055\u20132058"},{"key":"10089_CR49","doi-asserted-by":"crossref","unstructured":"Xiong C, Power R, Callan J (2017) Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th international conference on world wide web, pp 1271\u20131279","DOI":"10.1145\/3038912.3052558"},{"key":"10089_CR50","doi-asserted-by":"crossref","unstructured":"Xin J, Lin Y, Liu Z, Sun M (2018) Improving neural fine-grained entity typing with knowledge attention. In: AAAI, pp 1\u20138","DOI":"10.1609\/aaai.v32i1.12038"},{"key":"10089_CR51","doi-asserted-by":"crossref","unstructured":"Xu C, Wang F, Han J, Li C (2019) Exploiting multiple embeddings for chinese named entity recognition. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 2269\u20132272","DOI":"10.1145\/3357384.3358117"},{"key":"10089_CR52","doi-asserted-by":"crossref","unstructured":"Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K (2020) Product knowledge graph embedding for e-commerce. In: Proceedings of the 13th international conference on web search and data mining, pp 672\u2013680","DOI":"10.1145\/3336191.3371778"},{"key":"10089_CR53","doi-asserted-by":"crossref","unstructured":"Yao X, Van Durme B (2014) Information extraction over structured data: question answering with freebase. In: ACL, pp 956\u2013966","DOI":"10.3115\/v1\/P14-1090"},{"issue":"2","key":"10089_CR54","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","volume":"26","author":"Y Yao","year":"2007","unstructured":"Yao Y, Rosasco L, Caponnetto A (2007) On early stopping in gradient descent learning. Construct Approx 26(2):289\u2013315","journal-title":"Construct Approx"},{"key":"10089_CR55","unstructured":"Yao L, Torabi A, Cho K, Ballas N, Pal C, Larochelle H, Courville A (2015) Video description generation incorporating spatio-temporal features and a soft-attention mechanism. arXiv:1502.08029"},{"key":"10089_CR56","unstructured":"Yubo C, Liheng X, Kang L, Daojian Z, Jun Z et al (2015) Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL, pp 167\u2013176"},{"key":"10089_CR57","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J (2018) Chinese ner using lattice lstm. arXiv:1805.02023","DOI":"10.18653\/v1\/P18-1144"},{"key":"10089_CR58","doi-asserted-by":"crossref","unstructured":"Zhao J -X, Cao Y, Fan D -P, Cheng M -M, Li X -Y, Zhang L (2019) Contrast prior and fluid pyramid integration for rgbd salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3927\u20133936","DOI":"10.1109\/CVPR.2019.00405"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10089-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-10089-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10089-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T10:34:00Z","timestamp":1670754840000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-10089-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,12]]},"references-count":58,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["10089"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10089-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,1,12]]},"assertion":[{"value":"7 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}