{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:54:47Z","timestamp":1776095687357,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11280-7","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T19:01:45Z","timestamp":1691694105000},"page":"7689-7707","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enriching Word Information Representation for Chinese Cybersecurity Named Entity Recognition"],"prefix":"10.1007","volume":"55","author":[{"given":"Dongying","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Lian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6570-6245","authenticated-orcid":false,"given":"Wen","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cai","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"11280_CR1","doi-asserted-by":"crossref","unstructured":"Appelt D, Hobbs JR, Bear J, et\u00a0al (1995) SRI international FASTUS system: MUC-6 test results and analysis. In: Sixth message understanding conference (MUC-6): proceedings of a conference held in Columbia, Maryland, November 6\u20138, 1995","DOI":"10.3115\/1072399.1072420"},{"issue":"1","key":"11280_CR2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1023\/A:1007558221122","volume":"34","author":"DM Bikel","year":"1999","unstructured":"Bikel DM, Schwartz R, Weischedel RM (1999) An algorithm that learns what\u2019s in a name. Mach Learn 34(1):211\u2013231","journal-title":"Mach Learn"},{"key":"11280_CR3","unstructured":"Cetoli A, Bragaglia S, O\u2019Harney A, et\u00a0al (2017) Graph convolutional networks for named entity recognition. In: Proceedings of the 16th international workshop on treebanks and linguistic theories, Prague, Czech Republic, pp 37\u201345. https:\/\/aclanthology.org\/W17-7607"},{"issue":"3","key":"11280_CR4","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/08874417.2015.11645768","volume":"55","author":"SJ Conlon","year":"2015","unstructured":"Conlon SJ, Abrahams AS, Simmons LL (2015) Terrorism information extraction from online reports. J Comput Inf Syst 55(3):20\u201328. https:\/\/doi.org\/10.1080\/08874417.2015.11645768","journal-title":"J Comput Inf Syst"},{"issue":"19","key":"11280_CR5","doi-asserted-by":"publisher","first-page":"3945","DOI":"10.3390\/app9193945","volume":"9","author":"H Gasmi","year":"2019","unstructured":"Gasmi H, Laval J, Bouras A (2019) Information extraction of cybersecurity concepts: an LSTM approach. Appl Sci 9(19):3945. https:\/\/doi.org\/10.3390\/app9193945","journal-title":"Appl Sci"},{"key":"11280_CR6","doi-asserted-by":"publisher","unstructured":"Ghazi Y, Anwar Z, Mumtaz R, et\u00a0al (2018) A supervised machine learning based approach for automatically extracting high-level threat intelligence from unstructured sources. In: 2018 International conference on frontiers of information technology (FIT). IEEE, pp 129\u2013134. https:\/\/doi.org\/10.1109\/fit.2018.00030","DOI":"10.1109\/fit.2018.00030"},{"key":"11280_CR7","doi-asserted-by":"publisher","unstructured":"Gomez-Hidalgo JM, Mart\u00edn-Abreu JM, Nieves J, et\u00a0al (2010) Data leak prevention through named entity recognition. In: 2010 IEEE second international conference on social computing. IEEE, pp 1129\u20131134. https:\/\/doi.org\/10.1109\/socialcom.2010.167","DOI":"10.1109\/socialcom.2010.167"},{"key":"11280_CR8","doi-asserted-by":"publisher","unstructured":"Guo Q, Qiu X, Liu P, et\u00a0al (2019) Star-Transformer. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). Association for Computational Linguistics, Minneapolis, Minnesota, pp 1315\u20131325. https:\/\/doi.org\/10.18653\/v1\/N19-1133","DOI":"10.18653\/v1\/N19-1133"},{"key":"11280_CR9","doi-asserted-by":"publisher","unstructured":"Hammerton J (2003) Named entity recognition with long short-term memory. In: Proceedings of the seventh conference on natural language learning at HLT-NAACL, vol 2003, pp 172\u2013175. https:\/\/doi.org\/10.3115\/1119176.1119202","DOI":"10.3115\/1119176.1119202"},{"key":"11280_CR10","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: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"11280_CR11","doi-asserted-by":"publisher","first-page":"132,367","DOI":"10.1109\/ACCESS.2020.3002863","volume":"8","author":"J Hou","year":"2020","unstructured":"Hou J, Li X, Yao H et al (2020) BERT-based Chinese relation extraction for public security. IEEE Access 8:132,367-132,375. https:\/\/doi.org\/10.1109\/ACCESS.2020.3002863","journal-title":"IEEE Access"},{"key":"11280_CR12","unstructured":"Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. CoRR arXiv:1508.01991"},{"key":"11280_CR13","doi-asserted-by":"publisher","unstructured":"Husari G, Niu X, Chu B, et\u00a0al (2018) Using entropy and mutual information to extract threat actions from cyber threat intelligence. In: 2018 IEEE international conference on intelligence and security informatics (ISI). IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/isi.2018.8587343","DOI":"10.1109\/isi.2018.8587343"},{"key":"11280_CR14","doi-asserted-by":"publisher","unstructured":"Isozaki H, Kazawa H (2002) Efficient support vector classifiers for named entity recognition. In: COLING 2002: the 19th international conference on computational linguistics. https:\/\/doi.org\/10.3115\/1072228.1072282","DOI":"10.3115\/1072228.1072282"},{"issue":"1","key":"11280_CR15","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.eng.2018.01.004","volume":"4","author":"Y Jia","year":"2018","unstructured":"Jia Y, Qi Y, Shang H et al (2018) A practical approach to constructing a knowledge graph for cybersecurity. Engineering 4(1):53\u201360. https:\/\/doi.org\/10.1016\/j.eng.2018.01.004","journal-title":"Engineering"},{"key":"11280_CR16","doi-asserted-by":"publisher","unstructured":"Joshi A, Lal R, Finin T, et\u00a0al (2013) Extracting cybersecurity related linked data from text. In: 2013 IEEE seventh international conference on semantic computing. IEEE, pp 252\u2013259. https:\/\/doi.org\/10.1109\/icsc.2013.50","DOI":"10.1109\/icsc.2013.50"},{"key":"11280_CR17","doi-asserted-by":"crossref","unstructured":"Kim JH, Woodland P (2000) A rule-based named entity recognition system for speech input. pp 528\u2013531","DOI":"10.21437\/ICSLP.2000-131"},{"key":"11280_CR18","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. In: International conference on learning representations"},{"key":"11280_CR19","unstructured":"Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, ICML \u201901. Morgan Kaufmann Publishers Inc., San Francisco, pp 282\u2013289"},{"key":"11280_CR20","unstructured":"Lal R (2013) Information extraction of security related entities and concepts from unstructured text. Master\u2019s thesis, University of Maryland Baltimore County"},{"key":"11280_CR21","doi-asserted-by":"publisher","unstructured":"Landauer M, Skopik F, Wurzenberger M, et\u00a0al (2019) A framework for cyber threat intelligence extraction from raw log data. In: 2019 IEEE international conference on big data (big data). IEEE, pp 3200\u20133209. https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006328","DOI":"10.1109\/bigdata47090.2019.9006328"},{"key":"11280_CR22","doi-asserted-by":"publisher","unstructured":"Li S, Zhao Z, Hu R, et\u00a0al (2018) Analogical reasoning on Chinese morphological and semantic relations. In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 2: short papers). Association for Computational Linguistics, Melbourne, pp 138\u2013143. https:\/\/doi.org\/10.18653\/v1\/P18-2023","DOI":"10.18653\/v1\/P18-2023"},{"key":"11280_CR23","doi-asserted-by":"publisher","unstructured":"Li X, Yan H, Qiu X, et\u00a0al (2020) FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 6836\u20136842. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.611","DOI":"10.18653\/v1\/2020.acl-main.611"},{"key":"11280_CR24","doi-asserted-by":"publisher","unstructured":"Ling W, Dyer C, Black AW, et\u00a0al (2015) Finding function in form: compositional character models for open vocabulary word representation. In: Proceedings of the 2015 conference on empirical methods in natural language processing. Association for Computational Linguistics, Lisbon, pp 1520\u20131530. https:\/\/doi.org\/10.18653\/v1\/D15-1176","DOI":"10.18653\/v1\/D15-1176"},{"key":"11280_CR25","doi-asserted-by":"publisher","unstructured":"Liu H, Song J, Peng W, et\u00a0al (2022) TFM: A triple fusion module for integrating lexicon information in Chinese named entity recognition. Neural Process Lett 1\u201318. https:\/\/doi.org\/10.1007\/s11063-022-10768-y","DOI":"10.1007\/s11063-022-10768-y"},{"key":"11280_CR26","doi-asserted-by":"publisher","unstructured":"Ma R, Peng M, Zhang Q, et\u00a0al (2020) Simplify the usage of lexicon in Chinese NER. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 5951\u20135960. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.528","DOI":"10.18653\/v1\/2020.acl-main.528"},{"key":"11280_CR27","doi-asserted-by":"publisher","unstructured":"Ma X, Hovy E (2016) End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers). Association for Computational Linguistics, Berlin, pp 1064\u20131074. https:\/\/doi.org\/10.18653\/v1\/P16-1101","DOI":"10.18653\/v1\/P16-1101"},{"key":"11280_CR28","doi-asserted-by":"publisher","unstructured":"Marcheggiani D, Titov I (2017) Encoding sentences with graph convolutional networks for semantic role labeling. In: Proceedings of the 2017 conference on empirical methods in natural language processing. Association for Computational Linguistics, Copenhagen, pp 1506\u20131515. https:\/\/doi.org\/10.18653\/v1\/D17-1159","DOI":"10.18653\/v1\/D17-1159"},{"key":"11280_CR29","doi-asserted-by":"publisher","unstructured":"McCallum A, Li W (2003) Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of the seventh conference on natural language learning at HLT-NAACL, vol 2003, pp 188\u2013191. https:\/\/doi.org\/10.3115\/1119176.1119206","DOI":"10.3115\/1119176.1119206"},{"key":"11280_CR30","doi-asserted-by":"publisher","unstructured":"Mulwad V, Li W, Joshi A, et\u00a0al (2011) Extracting information about security vulnerabilities from web text. In: 2011 IEEE\/WIC\/ACM international conferences on web intelligence and intelligent agent technology. IEEE, pp 257\u2013260. https:\/\/doi.org\/10.1109\/wi-iat.2011.26","DOI":"10.1109\/wi-iat.2011.26"},{"key":"11280_CR31","doi-asserted-by":"publisher","unstructured":"Peng N, Dredze M (2016) Improving named entity recognition for Chinese social media with word segmentation representation learning. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers). Association for Computational Linguistics, Berlin, pp 149\u2013155. https:\/\/doi.org\/10.18653\/v1\/P16-2025","DOI":"10.18653\/v1\/P16-2025"},{"key":"11280_CR32","unstructured":"Souza F, Nogueira RF, de\u00a0Alencar\u00a0Lotufo R (2019) Portuguese named entity recognition using BERT-CRF. CoRR arXiv:1909.10649"},{"key":"11280_CR33","doi-asserted-by":"crossref","unstructured":"Szarvas G, Farkas R, Kocsor A (2006) A multilingual named entity recognition system using boosting and c4.5 decision tree learning algorithms. In: International conference on discovery science. Springer, pp 267\u2013278","DOI":"10.1007\/11893318_27"},{"key":"11280_CR34","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, NIPS\u201917. Curran Associates Inc., Red Hook, pp 6000\u20136010. https:\/\/doi.org\/10.5555\/3295222.3295349","DOI":"10.5555\/3295222.3295349"},{"issue":"3","key":"11280_CR35","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.1007\/s11063-019-10044-6","volume":"50","author":"W Wang","year":"2019","unstructured":"Wang W, Bao F, Gao G (2019) Learning morpheme representation for Mongolian named entity recognition. Neural Process Lett 50(3):2647\u20132664. https:\/\/doi.org\/10.1007\/s11063-019-10044-6","journal-title":"Neural Process Lett"},{"key":"11280_CR36","doi-asserted-by":"publisher","unstructured":"Wang Y, Sun Y, Ma Z, et\u00a0al (2020) Application of pre-training models in named entity recognition. In: 2020 12th International conference on intelligent human\u2013machine systems and cybernetics (IHMSC), pp 23\u201326. https:\/\/doi.org\/10.1109\/IHMSC49165.2020.00013","DOI":"10.1109\/IHMSC49165.2020.00013"},{"key":"11280_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6629591","volume":"2021","author":"B Xie","year":"2021","unstructured":"Xie B, Shen G, Guo C et al (2021) The named entity recognition of Chinese cybersecurity using an active learning strategy. Wirel Commun Mob Comput 2021:1\u201311. https:\/\/doi.org\/10.1155\/2021\/6629591","journal-title":"Wirel Commun Mob Comput"},{"key":"11280_CR38","unstructured":"Yan H, Deng B, Li X, et\u00a0al (2019) TENER: adapting transformer encoder for named entity recognition. CoRR arXiv:1911.04474"},{"issue":"5","key":"11280_CR39","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1007\/s11063-021-10547-1","volume":"53","author":"R Yan","year":"2021","unstructured":"Yan R, Jiang X, Dang D (2021) Named entity recognition by using XLNet-BiLSTM-CRF. Neural Process Lett 53(5):3339\u20133356. https:\/\/doi.org\/10.1007\/s11063-021-10547-1","journal-title":"Neural Process Lett"},{"issue":"1","key":"11280_CR40","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1023\/A:1009982220290","volume":"1","author":"Y Yang","year":"1999","unstructured":"Yang Y (1999) An evaluation of statistical approaches to text categorization. Inf Retr 1(1):69\u201390","journal-title":"Inf Retr"},{"key":"11280_CR41","unstructured":"Zhang S, Wang L, Sun K, et\u00a0al (2020) A practical Chinese dependency parser based on a large-scale dataset. CoRR arXiv:2009.00901"},{"key":"11280_CR42","doi-asserted-by":"publisher","unstructured":"Zhang Y, Yang J (2018) Chinese NER using lattice LSTM. In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers). Association for Computational Linguistics, Melbourne, pp 1554\u20131564. https:\/\/doi.org\/10.18653\/v1\/P18-1144","DOI":"10.18653\/v1\/P18-1144"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11280-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11280-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11280-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T19:23:01Z","timestamp":1698520981000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11280-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11280"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11280-7","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]},"assertion":[{"value":"13 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}