A network security entity recognition method based on feature template and CNN-BiLSTM-CRF
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Bergstra J, Bengio Y, 2012. Random search for hyperparameter optimization. J Mach Learn Res, 13(1):281–305.
Chiu JPC, Nichols E, 2015. Named entity recognition with bidirectional LSTM-CNNs. https://doi.org/arxiv.org/abs/1511.08308
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. https://doi.org/10.1145/1390156.1390177
Collobert R, Weston J, Bottou L, et al., 2011. Natural language processing (almost) from scratch. J Mach Learn Res, 12(1):2493–2537.
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. https://doi.org/10.1007/978-3-319-50496-4_20
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. https://doi.org/10.18653/v1/w15-3904
Feng YH, Yu H, Sun G, et al., 2018. Named entity recognition method based on BLSTM. Comput Sci, 45(2):261–268 (in Chinese). https://doi.org/10.11896/j.issn.1002-137X.2018.02.045
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. https://doi.org/10.3115/1620754.1620802
Gers FA, Schmidhuber A, Cummins F, 2000. Learning to forget: continual prediction with LSTM. Neur Comput, 12(10):2451–2471. https://doi.org/10.1162/089976600300015015
Goller C, Kuchler A, 1996. Learning task-dependent distributed representations by backpropagation through structure. Proc Int Conf on Neural Networks, p. 347–352. https://doi.org/10.1109/icnn.1996.548916
Hammerton J, 2003. Named entity recognition with long short-term memory. Proc 7th Conf on Natural Language Learning at HLT-NAACL, p. 172–175. https://doi.org/10.3115/1119176.1119202
Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang ZH, Wei X, Kai Y, 2015. Bidirectional LSTM-CRF models for sequence tagging. https://doi.org/arxiv.org/abs/1508.01991
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. https://doi.org/10.1109/icsc.2013.50
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. https://doi.org/10.3115/1117601.1117634
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.
Lample G, Ballesteros M, Subramanian S, et al., 2016. Neural architectures for named entity recognition. Proc NAACLHLT, p. 260–270. https://doi.org/10.18653/v1/N16-1030
LéCun Y, Bottou L, Bengio Y, et al., 1998. Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278–2324. https://doi.org/10.1109/5.726791
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). https://doi.org/10.11959/j.issn.2096-109x.2016.00028
Liu W, Li Y, Duan H, et al., 2016. Knowledge graph construction techniques. J Comput Res Dev, 53(3):582–600 (in Chinese). https://doi.org/10.7544/issn1000-1239.2016.20148228
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. https://doi.org/10.18653/v1/d15-1104
Ma XZ, Hovy E, 2016. End-to-end sequence labeling via bidirectional LSTM-CNNs-CRF. https://doi.org/10.18653/v1/p16-1101
Mikolov T, Chen K, Corrado G, et al., 2013a. Efficient estimation of word representations in vector space. https://doi.org/arxiv.org/abs/1301.3781
Mikolov T, Sutskever I, Chen K, et al., 2013b. Distributed representations of words and phrases and their compositionality. https://doi.org/arxiv.org/abs/1310.4546
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. https://doi.org/10.3115/v1/w14-1609
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. https://doi.org/10.18653/v1/d15-1064
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. https://doi.org/10.3115/v1/d14-1162
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.
Qiu QQ, Miao DQ, Zhang ZF, 2013. Named entity recognition on Chinese microblog. Comput Sci, 40(6):196–198 (in Chinese). https://doi.org/10.3969/j.issn.1002-137X.2013.06.042
Rabiner LR, 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE, 77(2):257–286. https://doi.org/10.1109/5.18626
Tang BZ, Cao HX, Wang XL, et al., 2014. Evaluating word representation features in biomedical named entity recognition tasks. Biomed Res Int, 2014:240403. https://doi.org/10.1155/2014/240403
Yang YM, 1999. An evaluation of statistical approaches to text categorization. Inform Retriev, 1(1–2):69–90. https://doi.org/10.1023/A:1009982220290
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). https://doi.org/10.3321/j.issn:1000-436X.2006.02.013