Abstract
Named entity recognition (NER) is a basic task to construct knowledge graph. The training performance is limited with few labelled data. One solution is active learning, which can achieve ideal results by multi-round sampling strategy to augment unlabelled data. However, there is very few labelled data in the early rounds, which leads to slow improvement on training performance. We thus propose a framework of data augmentation while active learning. To validate our claims, we focus on Chinese NER task and carry out extensive experiments on two public datasets. Experimental results show that our framework is effective for a series of classical query strategy. We can achieve 99% of the best deep model trained on full data using only 22% of the data on Resume, 63% labelled data is reduced as compared to pure active learning (PAL).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, T., Ma, Z., Zheng, H., Zhou, Y.: NE–LP: normalized entropy- and loss prediction-based sampling for active learning in Chinese word segmentation on EHRs. Neural Comput. Appl., 1–15 (2021). https://doi.org/10.1007/s00521-021-05896-w
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNS. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)
Culotta, A., McCallum, A.: Reducing labeling effort for structured prediction tasks, AAAI, vol. 5, pp. 746–751 (2005)
Dai, X., Adel, H.: An analysis of simple data augmentation for named entity recognition. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 3861–3867 (2020)
Ding, B., et al.: Daga: Data augmentation with a generation approach for low-resource tagging tasks (2020)
Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H.: Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 239–250. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_20
Gal, Y., Islam, R., Ghahramani, Z.: Deep Bayesian active learning with image data. In: International Conference on Machine Learning, pp. 1183–1192. PMLR (2017)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging
Kumar, V., Choudhary, A., Cho, E.: Data augmentation using pre-trained transformer models. In: Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems, pp. 18–26 (2020)
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT, pp. 260–270 (2016)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)
Liu, M., Tu, Z., Wang, Z., Xu, X.: LTP: A new active learning strategy for BERT-CRF based named entity recognition. arXiv preprint arXiv:2001.02524 (2020)
Ma, R., Peng, M., Zhang, Q., Wei, Z., Huang, X.J.: Simplify the usage of lexicon in Chinese NER. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5951–5960 (2020)
Marcheggiani, D., Artieres, T.: An experimental comparison of active learning strategies for partially labeled sequences. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 898–906 (2014)
Peng, N., Dredze, M.: Named entity recognition for Chinese social media with jointly trained embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 548–554 (2015)
Shen, D., Zhang, J., Su, J., Zhou, G., Tan, C.L.: Multi-criteria-based active learning for named entity recognition. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-2004), pp. 589–596 (2004)
Shen, Y., Yun, H., Lipton, Z.C., Kronrod, Y., Anandkumar, A.: Deep active learning for named entity recognition. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, pp. 252–256 (2017)
Tenney, I., Das, D., Pavlick, E.: BERT rediscovers the classical NLP pipeline. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4593–4601 (2019)
Yang, Z., Salakhutdinov, R., Cohen, W.: Multi-task cross-lingual sequence tagging from scratch. arXiv preprint arXiv:1603.06270 (2016)
Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1554–1564 (2018)
Acknowledgment
This work is supported by the National Key R&D Program of China under Grants (No. 2018YFB0204300).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Q., Huang, Z., Dou, Y., Zhang, Z. (2021). A Framework of Data Augmentation While Active Learning for Chinese Named Entity Recognition. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-82147-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82146-3
Online ISBN: 978-3-030-82147-0
eBook Packages: Computer ScienceComputer Science (R0)