| ［1］ |
ZHANG L， ZHAO H. Named entity recognition for Chinese microblog with convolutional neural network［C］// 2017 13th International Conference on Natural Computation， Fuzzy Systems and Knowledge Discovery （ICNC-FSKD）. IEEE， 2017:87-92.
|
| ［2］ |
陈宇，郑德权，赵铁军. 基于Deep Belief Nets的中文名实体关系抽取［J］. 软件学报， 2012，23（10）:2572-2585.
|
| ［3］ |
CUCERZAN S， YAROWSKY D. Language independent named entity recognition combining morphological and contextual evidence［C］// 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. 1999:90-99.
|
| ［4］ |
ZHOU G D， SU J. Named entity recognition using an HMM-based chunk tagger［C］// Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 2002:473-480.
|
| ［5］ |
LIU X H， ZHANG S D， WEI F R， et al. Recognizing named entities in tweets［C］// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011:359-367.
|
| ［6］ |
LAMPLE G， BALLESTEROS M， SUBRAMANIAN S， et al. Neural architectures for named entity recognition ［C］// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016:260-270.
|
| ［7］ |
柏兵，侯霞，石松. 基于CRF和BI-LSTM的命名实体识别方法［J］. 北京信息科技大学学报（自然科学版）， 2018，33（6）:27-33.
|
| ［8］ |
MA X Z， HOVY E. End-to-end sequence labeling via bi-directional LSTM-CNNS-CRF［C］// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016:1064-1074.
|
| ［9］ |
李明扬，孔芳. 融入自注意力机制的社交媒体命名实体识别［J］. 清华大学学报（自然科学版）， 2019，59（6）:461-467.
|
| ［10］ |
VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［C］// Proceedings of the Advances in Neural Information Processing Systems. 2017:5998-6008.
|
| ［11］ |
秦娅，申国伟，赵文波，等. 基于深度神经网络的网络安全实体识别方法［J］. 南京大学学报（自然科学版）， 2019，55（1）:29-40.
|
| ［12］ |
SOCHER R， HUVAL B， MANNING C D， et al. Semantic compositionality through recursive matrix-vector spaces［C］// Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012:1201-1211.
|
| ［13］ |
ZENG D J， LIU K， CHEN Y B， et al. Distant supervision for relation extraction via piecewise convolutional neural networks［C］// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015:1753-1762.
|
| ［14］ |
ZHOU P， SHI W， TIAN J， et al. Attention-based bidirectional long short-term memory networks for relation classification［C］// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016:207-212.
|
| ［15］ |
鄂海红，张文静，肖思琪，等. 深度学习实体关系抽取研究综述［J］. 软件学报， 2019，30（6）:1793-1818.
|
| ［16］ |
MIWA M， BANSAL M. End-to-end relation extraction using LSTMs on sequences and tree structures［C］// Proceedings of the Meeting of the Association for Computational Linguistics. 2016:1105-1116.
|
| ［17］ |
LI F， ZHANG M S， FU G H， et al. A neural joint model for extracting bacteria and their locations［C］// Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer， 2017:15-26.
|
| ［18］ |
KATIYAR A， CARDIE C. Going out on a limb: Joint extraction of entity mentions and relations without dependency trees［C］// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017:917-928.
|
| ［19］ |
ZHENG S C， WANG F， BAO H Y， et al. Joint extraction of entities and relations based on a novel tagging scheme［C］// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics（ACL 2017）. 2017:1227-1236.
|
| ［20］ |
ZENG X R， ZENG D J， HE S Z， et al. Extracting relational facts by an end-to-end neural model with copy mechanism［C］// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018:506-514.
|
| ［21］ |
FU T J， LI P H， MA W Y. Graphrel: Modeling text as relational graphs for joint entity and relation extraction［C］// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019:1409-1418.
|
| ［22］ |
ZENG X R， HE S Z， ZENG D J， et al. Learning the extraction order of multiple relational facts in a sentence with reinforcement learning［C］// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing（EMNLP-IJCNLP）. 2019:367-377.
|
| ［23］ |
HANG T T， FENG J， WU Y R， et al. Joint extraction of entities and overlapping relations using source-target entity labeling［J］. Expert Systems with Applications， 2021， 177: 114853.1-114853.15.
|
| ［24］ |
YE H B， ZHANG N Y， DENG S M， et al. Contrastive triple extraction with generative transformer［C］// Proceedings of the AAAI Conference on Artificial Intelligence. 2021:14257-14265.
|
| ［25］ |
WEI Z P， SU J L， WANG Y， et al. A novel cascade binary tagging framework for relational triple extraction［C］// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020:1476-1488.
|
| ［26］ |
DEVLIN J， CHANG M W， LEE K， et al. BERT: Pretraining of deep bidirectional transformers for language understanding［C］// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019:4171-4186.
|
| ［27］ |
GOODFELLOW I J，SHLENS J，SZEGEDY C. Explaining and harnessing adversarial examples［J］. arXiv preprint arXiv:1412.6572， 2014.
|
| ［28］ |
ZENG X R， ZENG D J， HE S E， et al. Extracting relational facts by an end-to-end neural model with copy mechanism［C］// Proceedings of the 56th Annual Meeting of the ACL. 2018:506-514.
|
