CLC number: TP393.08
On-line Access: 2019-07-08
Received: 2018-08-31
Revision Accepted: 2019-03-11
Crosschecked: 2019-06-11
Cited: 0
Clicked: 6114
Ya Qin, Guo-wei Shen, Wen-bo Zhao, Yan-ping Chen, Miao Yu, Xin Jin. A network security entity recognition method based on feature template and CNN-BiLSTM-CRF[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1800520 @article{title="A network security entity recognition method based on feature template and CNN-BiLSTM-CRF", %0 Journal Article TY - JOUR
一种基于特征模板和CNN-BiLSTM-CRF的网络安全实体识别方法关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Bergstra J, Bengio Y, 2012. Random search for hyperparameter optimization. J Mach Learn Res, 13(1):281-305. [2]Chiu JPC, Nichols E, 2015. Named entity recognition with bidirectional LSTM-CNNs. https://arxiv.org/abs/1511.08308 [3]Collobert R, Weston J, 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. Proc ACM 25th Int Conf on Machine Learning, p.160-167. [4]Collobert R, Weston J, Bottou L, et al., 2011. Natural language processing (almost) from scratch. J Mach Learn Res, 12(1):2493-2537. [5]Dong CH, Zhang JJ, Zong CQ, et al., 2016. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Lin CY, Xue N, Zhao D, et al. (Eds.), Natural Language Understanding and Intelligent Applications. Springer, Cham, p.239-250. [6]Dos Santos C, Guimarães V, 2015. Boosting named entity recognition with neural character embeddings. Proc 5th Named Entity Workshop, joint with 53rd ACL and the 7th IJCNLP, p.25-33. [7]Feng YH, Yu H, Sun G, et al., 2018. Named entity recognition method based on BLSTM. Comput Sci, 45(2):261-268 (in Chinese). [8]Finkel JR, Manning CD, 2009. Joint parsing and named entity recognition. Human Language Technologies: the Annual Conf of the North American Chapter of the Association of Computational Linguistics, p.326-334. [9]Gers FA, Schmidhuber A, Cummins F, 2000. Learning to forget: continual prediction with LSTM. Neur Comput, 12(10):2451-2471. [10]Goller C, Kuchler A, 1996. Learning task-dependent distributed representations by backpropagation through structure. Proc Int Conf on Neural Networks, p.347-352. [11]Hammerton J, 2003. Named entity recognition with long short-term memory. Proc 7th Conf on Natural Language Learning at HLT-NAACL, p.172-175. [12]Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(8):1735-1780. [13]Huang ZH, Wei X, Kai Y, 2015. Bidirectional LSTM-CRF models for sequence tagging. https://arxiv.org/abs/1508.01991 [14]Joshi A, Lal R, Finin T, et al., 2013. Extracting cybersecurity related linked data from text. IEEE 7th Int Conf on Semantic Computing, p.252-259. [15]Koeling R, 2000. Chunking with maximum entropy models. Proc 2nd Workshop on Learning Language in Logic and the 4th Conf on Computational Natural Language Learning, p.139-141. [16]Lafferty JD, McCallum A, Pereira FCN, 2001. Conditional random fields: probabilistic models for segmenting and labeling sequence data. 18th Int Conf on Machine Learning, p.282-289. [17]Lample G, Ballesteros M, Subramanian S, et al., 2016. Neural architectures for named entity recognition. Proc NAACL- HLT, p.260-270. [18]LéCun Y, Bottou L, Bengio Y, et al., 1998. Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278-2324. [19]Li JH, 2016. Overview of the technologies of threat intelligence sensing, sharing and analysis in cyber space. Chin J Network Inform Secur, 2(2):16-29 (in Chinese). [20]Liu W, Li Y, Duan H, et al., 2016. Knowledge graph construction techniques. J Comput Res Dev, 53(3):582-600 (in Chinese). [21]Luo G, Huang XJ, Li CY, et al., 2015. Joint named entity recognition and disambiguation. Proc Conf on Empirical Methods in Natural Language Processing, p.879-888. [22]Ma XZ, Hovy E, 2016. End-to-end sequence labeling via bi- directional LSTM-CNNs-CRF. [23]Mikolov T, Chen K, Corrado G, et al., 2013a. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781 [24]Mikolov T, Sutskever I, Chen K, et al., 2013b. Distributed representations of words and phrases and their compositionality. https://arxiv.org/abs/1310.4546 [25]Passos A, Kumar V, McCallum A, 2014. Lexicon infused phrase embeddings for named entity resolution. Proc 18th Conf on Computational Language Learning, p.78-86. [26]Peng NY, Dredze M, 2015. Named entity recognition for Chinese social media with jointly trained embeddings. Proc Conf on Empirical Methods in Natural Language Processing, p.548-554. [27]Pennington J, Socher R, Manning C, 2014. Glove: global vectors for word representation. Proc Conf on Empirical Methods in Natural Language Processing, p.1532-1543. [28]Pham V, Bluche T, Kermorvant C, et al., 2014. Dropout improves recurrent neural networks for handwriting recognition. 14th Int Conf on Frontiers in Handwriting Recognition, p.285-290. [29]Qiu QQ, Miao DQ, Zhang ZF, 2013. Named entity recognition on Chinese microblog. Comput Sci, 40(6):196-198 (in Chinese). [30]Rabiner LR, 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE, 77(2):257-286. [31]Tang BZ, Cao HX, Wang XL, et al., 2014. Evaluating word representation features in biomedical named entity recognition tasks. Biomed Res Int, 2014:240403. [32]Yang YM, 1999. An evaluation of statistical approaches to text categorization. Inform Retriev, 1(1-2):69-90. [33]Yu HK, Zhang HP, Liu Q, et al., 2006. Chinese named entity identification using cascaded hidden Markov model. J Commun, 27(2):87-94 (in Chinese). [34]Zhang XY, Wang T, Chen HW, 2005. Research on named entity recognition. Comput Sci, 32(4):44-48 (in Chinese). Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>