Abstract
Lexicon plays a critical role in Chinese Named Entity Recognition (CNER). The major reason lies in that words in the lexicon, lexicon words for short, are highly related to entity mention boundaries. Most lexicon enhanced CNER approaches focus on introducing lexicon words to the input and hidden layers. However, existing lexicon enhanced methods make the method hard to be adaptable and put weak lexicon constraints on architectures. To tackle these challenge, we propose a unified lexicon enhanced CNER framework. Specifically, lexicon word identification (LWI) task is proposed to locate and classify textual references to lexicon words. Similar to CNER task, this task is formalized either as sequence labeling or character relation classification, adopts CRF or Co-Predictor (Character relation classification) as the task specific layer, and is optimized with log-likelihood function of sequence probability or cross entropy of character pair relation type distribution. LWI task shares the input and hidden layers with CNER task. The whole framework is pre-trained with LWI task and fine-tuned with CNER task. Experimental results on two benchmark CNER datasets show the better effectiveness and flexibility than state-of-the-art baselines.
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
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, Y.: Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo (2015)
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
Gui, T., Ma, R., Zhang, Q., Zhao, L., Jiang, Y.G., Huang, X.: CNN-based Chinese NER with lexicon rethinking. In: IJCAI, pp. 4982–4988 (2019)
Gui, T., et al.: 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 (2019)
He, H., Sun, X.: F-score driven max margin neural network for named entity recognition in Chinese social media. arXiv preprint arXiv:1611.04234 (2016)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)
Jia, Y., Ma, X.: Attention in character-based BiLSTM-CRF for Chinese named entity recognition. In: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence, pp. 1–4 (2019)
Li, J., et al.: Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 10965–10973 (2022)
Li, X., Yan, H., Qiu, X., Huang, X.: Flat: Chinese NER using flat-lattice transformer. arXiv preprint arXiv:2004.11795 (2020)
Lu, Y., Zhang, Y., Ji, D.: Multi-prototype Chinese character embedding. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC2016), pp. 855–859 (2016)
Ma, R., Peng, M., Zhang, Q., Huang, X.: Simplify the usage of lexicon in Chinese NER. arXiv preprint arXiv:1908.05969 (2019)
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)
Peng, N., Dredze, M.: Improving named entity recognition for Chinese social media with word segmentation representation learning. arXiv preprint arXiv:1603.00786 (2016)
Shen, Y., Yun, H., Lipton, Z.C., Kronrod, Y., Anandkumar, A.: Deep active learning for named entity recognition. arXiv preprint arXiv:1707.05928 (2017)
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 (2019)
Wu, F., Liu, J., Wu, C., Huang, Y., Xie, X.: Neural Chinese named entity recognition via CNN-LSTM-CRF and joint training with word segmentation. In: The World Wide Web Conference, pp. 3342–3348 (2019)
Yan, H., Deng, B., Li, X., Qiu, X.: TENER: adapting transformer encoder for named entity recognition. arXiv preprint arXiv:1911.04474 (2019)
Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. arXiv preprint arXiv:1805.02023 (2018)
Acknowledgements
This research work was funded by the National Natural Science Foundation of China under Grant No. 62072447.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, W., Zhang, C., Niu, S., Shi, L. (2023). Unify the Usage of Lexicon in Chinese Named Entity Recognition. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13945. Springer, Cham. https://doi.org/10.1007/978-3-031-30675-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-031-30675-4_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30674-7
Online ISBN: 978-3-031-30675-4
eBook Packages: Computer ScienceComputer Science (R0)