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Auxiliary Information Enhanced Span-Based Model for Nested Named Entity Recognition

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Natural Language Processing and Chinese Computing (NLPCC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14302))

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Abstract

Span-based methods have unique advantages for solving nested named entity recognition (NER) problems. As primary information, boundaries play a crucial role in span representation. However, auxiliary information, which assists in identifying entities, still needs to be adequately investigated. In this work, We propose a simple yet effective method to enhance classification performance using boundaries and auxiliary information. Our model mainly consists of an adaptive convolution layer, an information-aware layer, and an information-agnostic layer. Adaptive convolution layers dynamically acquire words at different distances to enhance position-aware head and tail representations of spans. Information-aware and information-agnostic layers selectively incorporate boundaries and auxiliary information into the span representation and maintain boundary-oriented. Experiments show that our method outperforms the previous span-based methods and achieves state-of-the-art \(F_{1}\) scores on four NER datasets named ACE2005, ACE2004, Weibo and Resume. Experiments also show comparable results on GENIA and CoNLL2003.

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References

  1. Cao, H., et al.: OneEE: a one-stage framework for fast overlapping and nested event extraction. In: Proceedings of COLING, pp. 1953–1964. International Committee on Computational Linguistics, Gyeongju, Republic of Korea (2022)

    Google Scholar 

  2. Cordonnier, J.B., Loukas, A., Jaggi, M.: On the relationship between self-attention and convolutional layers. arXiv preprint arXiv:1911.03584 (2019)

  3. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL, pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019)

    Google Scholar 

  4. Fisher, J., Vlachos, A.: Merge and label: a novel neural network architecture for nested NER. In: Proceedings of ACL, pp. 5840–5850. Association for Computational Linguistics, Florence, Italy (2019)

    Google Scholar 

  5. Gillick, D., Brunk, C., Vinyals, O., Subramanya, A.: Multilingual language processing from bytes. In: Proceedings of NAACL, pp. 1296–1306. Association for Computational Linguistics, San Diego, California (2016)

    Google Scholar 

  6. Gu, Y., Qu, X., Wang, Z., Zheng, Y., Huai, B., Yuan, N.J.: Delving deep into regularity: A simple but effective method for Chinese named entity recognition. In: Findings of NAACL, pp. 1863–1873. Association for Computational Linguistics, Seattle, United States (2022)

    Google Scholar 

  7. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  8. Huang, P., Zhao, X., Hu, M., Fang, Y., Li, X., Xiao, W.: Extract-select: a span selection framework for nested named entity recognition with generative adversarial training. In: Findings of ACL, pp. 85–96. Association for Computational Linguistics, Dublin, Ireland (2022)

    Google Scholar 

  9. Ju, M., Miwa, M., Ananiadou, S.: A neural layered model for nested named entity recognition. In: Proceedings of NAACL, pp. 1446–1459. Association for Computational Linguistics, New Orleans, Louisiana (2018)

    Google Scholar 

  10. Li, F., Lin, Z., Zhang, M., Ji, D.: A span-based model for joint overlapped and discontinuous named entity recognition. In: Proceedings of ACL-IJCNLP, pp. 4814–4828. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Li, X., Yan, H., Qiu, X., Huang, X.: FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of ACL, pp. 6836–6842. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  13. Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., Li, J.: A unified MRC framework for named entity recognition. In: Proceedings of ACL, pp. 5849–5859. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  14. Lu, W., Roth, D.: Joint mention extraction and classification with mention hypergraphs. In: Proceedings of EMNLP, pp. 857–867. Association for Computational Linguistics, Lisbon, Portugal (2015)

    Google Scholar 

  15. Ma, R., Peng, M., Zhang, Q., Wei, Z., Huang, X.: Simplify the usage of lexicon in Chinese NER. In: Proceedings of ACL, pp. 5951–5960. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  16. Muis, A.O., Lu, W.: Learning to recognize discontiguous entities. In: Proceedings of EMNLP, pp. 75–84. Association for Computational Linguistics, Austin, Texas (2016)

    Google Scholar 

  17. Muis, A.O., Lu, W.: Labeling gaps between words: recognizing overlapping mentions with mention separators. In: Proceedings of EMNLP, pp. 2608–2618. Association for Computational Linguistics, Copenhagen, Denmark (2017)

    Google Scholar 

  18. Nie, Y., Tian, Y., Song, Y., Ao, X., Wan, X.: Improving named entity recognition with attentive ensemble of syntactic information. In: Findings of EMNLP, pp. 4231–4245. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  19. Shen, Y., Ma, X., Tan, Z., Zhang, S., Wang, W., Lu, W.: Locate and label: a two-stage identifier for nested named entity recognition. In: Proceedings of ACL-IJCNLP, pp. 2782–2794. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  20. Su, J., Lu, Y., Pan, S., Wen, B., Liu, Y.: Roformer: enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864 (2021)

  21. Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6398–6407 (2020)

    Google Scholar 

  22. Wang, B., Lu, W.: Neural segmental hypergraphs for overlapping mention recognition. In: Proceedings of EMNLP, pp. 204–214. Association for Computational Linguistics, Brussels, Belgium (2018)

    Google Scholar 

  23. Wang, J., Shou, L., Chen, K., Chen, G.: Pyramid: a layered model for nested named entity recognition. In: Proceedings of ACL, pp. 5918–5928. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  24. Wang, X., et al.: Automated concatenation of embeddings for structured prediction. In: Proceedings of ACL-IJCNLP, pp. 2643–2660. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  25. Wang, X., et al.: Improving named entity recognition by external context retrieving and cooperative learning. In: Proceedings of ACL, pp. 1800–1812. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  26. Wang, Y., Yu, B., Zhu, H., Liu, T., Yu, N., Sun, L.: Discontinuous named entity recognition as maximal clique discovery. In: Proceedings ACL-IJCNLP, pp. 764–774. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  27. Xu, Y., Huang, H., Feng, C., Hu, Y.: A supervised multi-head self-attention network for nested named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 14185–14193 (2021)

    Google Scholar 

  28. Yan, H., Deng, B., Li, X., Qiu, X.: Tener: adapting transformer encoder for named entity recognition. arXiv preprint arXiv:1911.04474 (2019)

  29. Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., Qiu, X.: A unified generative framework for various NER subtasks. In: Proceedings of ACL-IJCNLP, pp. 5808–5822. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  30. Yan, H., Sun, Y., Li, X., Qiu, X.: An embarrassingly easy but strong baseline for nested named entity recognition. arXiv preprint arXiv:2208.04534 (2022), https://arxiv.53yu.com/pdf/2208.04534

  31. Yu, J., Bohnet, B., Poesio, M.: Named entity recognition as dependency parsing. In: Proceedings of ACL, pp. 6470–6476. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  32. Yuan, Z., Tan, C., Huang, S., Huang, F.: Fusing heterogeneous factors with triaffine mechanism for nested named entity recognition. In: Findings of ACL, pp. 3174–3186. Association for Computational Linguistics, Dublin, Ireland (2022)

    Google Scholar 

  33. Zhang, S., Cheng, H., Gao, J., Poon, H.: Optimizing bi-encoder for named entity recognition via contrastive learning. arXiv preprint arXiv:2208.14565 (2022)

  34. Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Proceedings of ACL, pp. 1554–1564. Association for Computational Linguistics, Melbourne, Australia (2018)

    Google Scholar 

  35. Zhu, E., Li, J.: Boundary smoothing for named entity recognition. In: Proceedings of ACL, pp. 7096–7108. Association for Computational Linguistics, Dublin, Ireland (2022)

    Google Scholar 

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Acknowledgement

This work was supported by the Jilin Provincial Department of Education Science and Technology Research Planning Project, Grant number jjkh20220779kj. Jilin Provincial Science and Technology Development Plan Project, Grant number 20220201149gx.

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Authors and Affiliations

  1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China

    Yiming Sun, Chenyang Li & Weihao Kong

Authors
  1. Yiming Sun
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  2. Chenyang Li
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  3. Weihao Kong
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Corresponding author

Correspondence to Yiming Sun .

Editor information

Editors and Affiliations

  1. Emory University, Atlanta, GA, USA

    Fei Liu

  2. Microsoft Research Asia, Beijing, China

    Nan Duan

  3. Soochow University, Suzhou, China

    Qingting Xu

  4. Soochow University, Suzhou, China

    Yu Hong

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Sun, Y., Li, C., Kong, W. (2023). Auxiliary Information Enhanced Span-Based Model for Nested Named Entity Recognition. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14302. Springer, Cham. https://doi.org/10.1007/978-3-031-44693-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-44693-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44692-4

  • Online ISBN: 978-3-031-44693-1

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