Abstract
Classical Chinese NER aims to automatically identify named entities in classical texts, which can effectively help people understand the content of classical texts. However, due to the difficulty of annotating classical Chinese texts, the scale of existing datasets seriously restricts the development of classical Chinese NER. To address this challenge, we propose an Adversarial Transfer for Classical Chinese NER (AT-CCNER) model, which transfers features learned from large-scale translation word segmentation to assist recognize classical Chinese named entities. In addition, to reduce the feature differences between modern and classical Chinese texts, AT-CCNER utilizes the adversarial method to better apply to classical Chinese texts. We experimentally demonstrate the effectiveness of our method on the open-source classical Chinese NER dataset C-CLUE. What’s more, we compare the effects of translation text of different scales on the experimental results. Our method improves Precision, Recall, and F1 by 3.61%, 3.45%, and 3.54%, respectively, compared to the BiLSTM-CRF model.
Y. Qi and H. Ma—Equal contribution.
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References
Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 724–731 (2005)
Cao, P., Chen, Y., Liu, K., Zhao, J., Liu, S.: Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 182–192 (2018)
Chen, C., Kong, F.: Enhancing entity boundary detection for better Chinese named entity recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 20–25 (2021)
CUI Dan-dan, LIU Xiu-lei, C.R.y.L.X.h.L.Z.Q.L.: Named entity recognition in filed of ancient Chinese based on lattice LSTM. Comput. Sci. 47(S02), 5 (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
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)
Goodfellow, I., et al.: Generative adversarial nets. Advances in neural information processing systems 27 (2014)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. Stat 1050, 20 (2015)
Gui, T., Zou, Y., Zhang, Q., Peng, M., Fu, J., Wei, Z., Huang, X.: A lexicon-based graph neural network for Chinese NER. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1040–1050. Association for Computational Linguistics, Hong Kong, China, November 2019
Ji, Z., Shen, Y., Sun, Y., Yu, T., Wang, X.: C-CLUE: a benchmark of classical Chinese based on a crowdsourcing system for knowledge graph construction. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds.) CCKS 2021. CCIS, vol. 1466, pp. 295–301. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-6471-7_24
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data (2001)
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)
Meftah, S., Semmar, N., Tahiri, M.A., Tamaazousti, Y., Essafi, H., Sadat, F.: Multi-task supervised pretraining for neural domain adaptation. In: Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media, pp. 61–71 (2020)
Mulcaire, P., Kasai, J., Smith, N.A.: Low-resource parsing with crosslingual contextualized representations. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pp. 304–315 (2019)
Panchendrarajan, R., Amaresan, A.: Bidirectional lstm-crf for named entity recognition. In: Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation (2018)
Peng, N., Dredze, M.: Improving named entity recognition for Chinese social media with word segmentation representation learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 149–155 (2016)
Ruder, S., Plank, B.: Strong baselines for neural semi-supervised learning under domain shift. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1044–1054 (2018)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
Shen, W., Wang, J., Luo, P., Wang, M.: Linden: linking named entities with knowledge base via semantic knowledge. In: Proceedings of the 21st international conference on World Wide Web, pp. 449–458 (2012)
Simpson, E., Pfeiffer, J., Gurevych, I.: Low resource sequence tagging with weak labels. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8862–8869 (2020)
Sui, D., Chen, Y., Liu, K., Zhao, J., Liu, S.: Leverage lexical knowledge for Chinese named entity recognition via collaborative graph network. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3830–3840. Association for Computational Linguistics, Hong Kong, China, November 2019
Wang, J., Xiao, L., L.J.Y.J.: Research on named entity recognition based on treatise on febrile diseases. Comput. Digit. Eng. 49(8), 4 (2021)
Wu, S., Song, X., Feng, Z.: Mect: Multi-metadata embedding based cross-transformer for Chinese named entity recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1529–1539 (2021)
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (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. Association for Computational Linguistics, Melbourne, Australia, July 2018
Zhou, J.T., et al.: Dual adversarial neural transfer for low-resource named entity recognition. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3461–3471 (2019)
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Qi, Y., Ma, H., Shi, L., Zan, H., Zhou, Q. (2022). Adversarial Transfer for Classical Chinese NER with Translation Word Segmentation. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https://doi.org/10.1007/978-3-031-17120-8_24
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