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http://dx.doi.org/10.9708/jksci.2020.25.04.029

DeNERT: Named Entity Recognition Model using DQN and BERT  

Yang, Sung-Min (Dept. of Software, Gachon University)
Jeong, Ok-Ran (Dept. of Software, Gachon University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.4, 2020 , pp. 29-35 More about this Journal
Abstract
In this paper, we propose a new structured entity recognition DeNERT model. Recently, the field of natural language processing has been actively researched using pre-trained language representation models with a large amount of corpus. In particular, the named entity recognition, which is one of the fields of natural language processing, uses a supervised learning method, which requires a large amount of training dataset and computation. Reinforcement learning is a method that learns through trial and error experience without initial data and is closer to the process of human learning than other machine learning methodologies and is not much applied to the field of natural language processing yet. It is often used in simulation environments such as Atari games and AlphaGo. BERT is a general-purpose language model developed by Google that is pre-trained on large corpus and computational quantities. Recently, it is a language model that shows high performance in the field of natural language processing research and shows high accuracy in many downstream tasks of natural language processing. In this paper, we propose a new named entity recognition DeNERT model using two deep learning models, DQN and BERT. The proposed model is trained by creating a learning environment of reinforcement learning model based on language expression which is the advantage of the general language model. The DeNERT model trained in this way is a faster inference time and higher performance model with a small amount of training dataset. Also, we validate the performance of our model's named entity recognition performance through experiments.
Keywords
Natural language processing; Named entity recognition; Reinforcement learning; BERT; DQN; Language model;
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1 G. Lample and M. Ballesteros, "Neural architectures for named entity recognition," Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.260-270, Jun. 2016. DOI: 10.18653/v1/N16-1030
2 ME. Peters and M. Neumann, "Deep contextualized word representations," Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp.2227-2237, Jun. 2018. DOI: 10.18653/v1/N18-1202
3 A. Radford and K. Narasimhan, "Improving language understanding by generative pre-training," URL https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
4 J. Devlin and MW. Chang, "Bert: Pre-training of deep bidirectional transformers for language understanding," 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), pp.4171-4186, Jun. 2019. DOI: 10.18653/v1/N19-1423
5 A. Vaswani and N. Shazeer, "Attention is all you need," Advances in neural information processing systems, Dec. 2017. https://arxiv.org/abs/1706.03762
6 CJCH. Watkins and P.Dayan, "Q-learning," Machine learning 8.3-4. pp. 279-292, May. 1992. DOI: 10.1007/BF00992698   DOI
7 V. Mnih and K. Kavukcuoglu, "Playing atari with deep reinforcement learning," arXiv preprint arXiv:1312.5602, Dec. 2013. https://arxiv.org/abs/1312.5602
8 V. Mnih and K. Kavukcuoglu, "Human-level control through deep reinforcement learning," Nature 518.7540, pp.529-533, Feb. 2015. DOI: 10.1038/nature14236   DOI
9 M. Fang and Y. Li, "Learning how to active learn: A deep reinforcement learning approach," Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 595-605. Sep. 2017. DOI: 10.18653/v1/D17-1063
10 Y. Yang and W. Chen, "Distantly supervised ner with partial annotation learning and reinforcement learning," Proceedings of the 27th International Conference on Computational Linguistics, pp. 2159-2169, Aug. 2018. https://www.aclweb.org/anthology/C18-1183.pdf
11 Z. Huang and W. Xu, "Bidirectional LSTM-CRF models for sequence tagging," Pro-ceedings of the 21st International Conference on AsianLanguage Processing, Aug. 2015. https://arxiv.org/abs/1508.01991
12 Y. Wu and M. Schuster, "Google's neural machine translation system: Bridging the gap between human and machine translation," arXiv preprint arXiv:1609.08144, Oct. 2016. https://arxiv.org/abs/1609.08144
13 CD. Manning and M. Surdeanu, "The Stanford CoreNLP natural language processing toolkit," Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pp.55-60, Jun. 2014. DOI: 10.3115/v1/P14-5010
14 EF. Sang and F. De. Meulder, "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition," Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL, pp.142-147, Jun. 2003. https://arxiv.org/abs/cs/0306050
15 M. Abadi and A. Agarwal, "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprint arXiv:1603.04467, Mar. 2016. https://arxiv.org/abs/1603.04467
16 A. Akbik and T. Bergmann, "Pooled contextualized embeddings for named entity recognition," Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Volume 1, pp. 724-728, Jun. 2019. DOI: 10.18653/v1/N19-1078
17 V. Mnih and AP. Badia, "Asynchronous methods for deep reinforcement learning," International conference on machine learning, Feb. 2016. https://arxiv.org/abs/1602.01783
18 T. Mikolov and I. Sutskever, "Distributed representations of words and phrases and their compositionality," Advances in neural information processing systems, pp. 3111-3119, Oct. 2013. DOI: 1310.4546
19 RS. Sutton and DA. McAllester, "Policy gradient methods for reinforcement learning with function approximation," Advances in neural information processing systems, pp. 1057-1063, Jun. 2000. https://arxiv.org/abs/1706.06643