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Research Article

Full-span named entity recognition with boundary regression

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Article: 2181483 | Received 26 Oct 2022, Accepted 12 Feb 2023, Published online: 02 Mar 2023

References

  • Alex, B., Haddow, B., & Grover, C. (2007). Recognising nested named entities in biomedical text. In Biological, translational, and clinical language processing (pp. 65–72).
  • Al-Moslmi, T., Ocaña, M. G., Opdahl, A. L., & Veres, C. (2020). Named entity extraction for knowledge graphs: A literature overview. IEEE Access, 8, 32862–32881. https://doi.org/10.1109/Access.6287639
  • Batbaatar, E., & Ryu, K. H. (2019). Ontology-based healthcare named entity recognition from twitter messages using a recurrent neural network approach. International Journal of Environmental Research and Public Health, 16(19), 3628. https://doi.org/10.3390/ijerph16193628
  • Brandsen, A., Verberne, S., Lambers, K., & Wansleeben, M. (2022, September). Can BERT dig it? named entity recognition for information retrieval in the archaeology domain. Journal of Computing and Cultural Heritage, 15(3), 1–18. https://doi.org/10.1145/3497842
  • Cetoli, A., Bragaglia, S., O'Harney, A., & Sloan, M. (2017). Graph convolutional networks for named entity recognition. In Proceedings of the 16th international workshop on treebanks and linguistic theories (pp. 37–45)..
  • Chen, Y., Wu, L., Zheng, Q., Huang, R., Liu, J., Deng, L., & Chen, P. (2022). A boundary regression model for nested named entity recognition. Cognitive Computation, 1–18. https://doi.org/10.1007/s12559-022-10058-8
  • Chen, Y., Wu, Y., Qin, Y., Hu, Y., Wang, Z., Huang, R., & Chen, P. (2019). Recognizing nested named entity based on the neural network boundary assembling model. IEEE Intelligent Systems, 35(1), 74–81. https://doi.org/10.1109/MIS.2019.2952334
  • Chiu, B., Crichton, G., Korhonen, A., & Pyysalo, S. (2016). How to train good word embeddings for biomedical NLP. In Proceedings of the 15th workshop on biomedical natural language processing (pp. 166–174).
  • Chiu, J. P., & Nichols, E. (2016). Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 4, 357–370. https://doi.org/10.1162/tacl_a_00104
  • Devlin, J., Chang, M. W.., Lee, K., & Toutanova, K. (2019, June). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies (pp. 4171–4186). Association for Computational Linguistics.
  • Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., & Weischedel, R. (2004, May). The Automatic Content Extraction (ACE) program – tasks, data, and evaluation. In Proceedings of the fourth international conference on language resources and evaluation. European Language Resources Association.
  • Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (VOC) challenge. In (Vol. 88, pp. 303–338). Springer.
  • Fisher, J., & Vlachos, A. (2019, July). Merge and label: A novel neural network architecture for nested NER. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 5840–5850). Association for Computational Linguistics.
  • Ghaddar, A., & Langlais, P. (2018, August). Robust lexical features for improved neural network named-entity recognition. In Proceedings of the 27th international conference on computational linguistics (pp. 1896–1907). Association for Computational Linguistics.
  • Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1440–1448).
  • Grishman, R., & Sundheim, B. M. (1996). Message understanding conference-6: A brief history. In Coling 1996 Vol. 1: The 16th international conference on computational linguistics.
  • Hu, J., Hayashi, H., Cho, K., & Neubig, G. (2022, May). DEEP: DEnoising Entity Pre-training for Neural Machine Translation. In Proceedings of the 60th annual meeting of the association for computational linguistics (pp. 1753–1766). Association for Computational Linguistics.
  • Jaiswal, A. K., Tiwari, P., Garg, S., & Hossain, M. S. (2021). Entity-aware capsule network for multi-class classification of big data: A deep learning approach. Future Generation Computer Systems, 117, 1–11. https://doi.org/10.1016/j.future.2020.11.012
  • Jie, Z., & Lu, W. (2019, November). Dependency-guided LSTM-CRF for named entity recognition. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (pp. 3862–3872). Association for Computational Linguistics.
  • Jie, Z., Muis, A. O., & Lu, W. (2017). Efficient dependency-guided named entity recognition. In Proceedings of the thirty-first aaai conference on artificial intelligence (pp. 3457-3465). AAAI Press.
  • Ju, M., Miwa, M., Ananiadou, S., & Ananiadou, S. (2018). A neural layered model for nested named entity recognition. In Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: Human language technologies (pp. 1446–1459).
  • Katiyar, A., & Cardie, C. (2018). Nested named entity recognition revisited. In Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: Human language technologies (pp. 861–871).
  • Kim, J. D., Ohta, T., Tateisi, Y., & Tsujii, J. (2003). Genia corpus – A semantically annotated corpus for bio-textmining. In Bioinformatics (pp. 180–182).
  • Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016, June). Neural architectures for named entity recognition. In Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: Human language technologies (pp. 260–270). Association for Computational Linguistics.
  • Li, J., Chiu, B., Feng, S., & Wang, H. (2020). Few-shot named entity recognition via meta-learning. IEEE Transactions on Knowledge and Data Engineering, 34(9), 4245–4256. https://doi.org/10.1109/tkde.2020.3038670
  • Li, J., Fei, H., Liu, J., Wu, S., Zhang, M., Teng, C., & Li, F. (2022). Unified named entity recognition as word-word relation classification. In Proceedings of the aaai conference on artificial intelligence (Vol. 36, pp. 10965–10973).
  • Li, J., Han, P., Ren, X., Hu, J., Chen, L., & Shang, S. (2021). Sequence labeling with meta-learning. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/tkde.2021.3118469
  • Li, J., Shang, S., & Chen, L. (2021). Domain generalization for named entity boundary detection via metalearning. IEEE Transactions on Neural Networks and Learning Systems, 32(9), 3819–3830. https://doi.org/10.1109/TNNLS.2020.3015912
  • Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020, July). A unified MRC framework for named entity recognition. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 5849–5859). Online. Association for Computational Linguistics.
  • Lin, H., Lu, Y., Han, X., & Sun, L. (2019, July). Sequence-to-nuggets: Nested entity mention detection via anchor-region networks. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 5182–5192). Association for Computational Linguistics.
  • Lin, H., Lu, Y., Han, X., Sun, L., Dong, B., & Jiang, S. (2019). Gazetteer-enhanced attentive neural networks for named entity recognition. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (pp. 6232–6237).
  • Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., & Singh, S. (2021). Entity-based knowledge conflicts in question answering. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 7052–7063).
  • Lou, C., Yang, S., & Tu, K. (2022, May). Nested named entity recognition as latent lexicalized constituency parsing. In Proceedings of the 60th annual meeting of the association for computational linguistics (pp. 6183–6198). Association for Computational Linguistics.
  • Lu, W., & Roth, D. (2015). Joint mention extraction and classification with mention hypergraphs. In Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 857–867).
  • Luo, Y., Xiao, F., & Zhao, H. (2020). Hierarchical contextualized representation for named entity recognition. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, pp. 8441–8448).
  • Ma, X., & Hovy, E. (2016, August). End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In Proceedings of the 54th annual meeting of the association for computational linguistics (pp. 1064–1074). Berlin, Germany: Association for Computational Linguistics.
  • Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning–based text classification: A comprehensive review. ACM Computing Surveys (CSUR), 54(3), 1–40. https://doi.org/10.1145/3439726
  • Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (emnlp) (pp. 1532–1543).
  • Pradhan, S., Moschitti, A., Xue, N., Ng, H. T., Björkelund, A., Uryupina, O., & Zhong, Z. (2013, August). Towards robust linguistic analysis using OntoNotes. In Proceedings of the seventeenth conference on computational natural language learning (pp. 143–152). Association for Computational Linguistics.
  • Shen, Y., Ma, X., Tan, Z., Zhang, S., Wang, W., & Lu, W. (2021, August). Locate and label: A two-stage identifier for nested 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 (pp. 2782–2794). Online. Association for Computational Linguistics.
  • Shen, Y., Wang, X., Tan, Z., Xu, G., Xie, P., Huang, F., & Zhuang, Y. (2022, May). Parallel instance query network for named entity recognition. In Proceedings of the 60th annual meeting of the association for computational linguistics (Vol. 1: Long papers) (pp. 947–961). Association for Computational Linguistics.
  • Shibuya, T., & Hovy, E. (2020). Nested named entity recognition via second-best sequence learning and decoding. Transactions of the Association for Computational Linguistics, 8, 605–620. https://doi.org/10.1162/tacl_a_00334
  • Sohrab, M. G., & Miwa, M. (2018). Deep exhaustive model for nested named entity recognition. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 2843–2849).
  • Straková, J., Straka, M., & Hajic, J. (2019, July). Neural architectures for nested NER through linearization. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 5326–5331). Association for Computational Linguistics.
  • Tan, C., Qiu, W., Chen, M., Wang, R., & Huang, F. (2020, April). Boundary enhanced neural span classification for nested named entity recognition. Proceedings of the AAAI Conference on Artificial Intelligence 34 (05) pp. 9016–9023.
  • Tjong Kim Sang, E. F., & De Meulder, F. (2003). Introduction to the CoNLL-2003 Shared Task: Language-independent named entity recognition. In Proceedings of the 2003 conference of the north American chapter of the association for computational linguistics: Human language technologies (pp. 142–147).
  • Wan, J., Ru, D., Zhang, W., & Yu, Y. (2022, May). Nested named entity recognition with span-level graphs. In Proceedings of the 60th annual meeting of the association for computational linguistics (Vol. 1: Long papers) (pp. 892–903). Association for Computational Linguistics.
  • Wang, B., & Lu, W. (2018, October-November). Neural Segmental Hypergraphs for Overlapping Mention Recognition. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 204–214). Association for Computational Linguistics.
  • Wang, J., Shou, L., Chen, K., & Chen, G. (2020). Pyramid: A layered model for nested named entity recognition. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 5918–5928).
  • Wang, X., Jiang, Y., Bach, N., Wang, T., Huang, Z., Huang, F., & Tu, K. (2021, August). Improving named entity recognition by external context retrieving and cooperative learning. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (pp. 1800–1812). Online. Association for Computational Linguistics.
  • Wang, Y., Tong, H., Zhu, Z., & Li, Y. (2022, July). Nested named entity recognition: a survey. ACM Transactions on Knowledge Discovery From Data, 16(6), 1–29. https://doi.org/10.1145/3522593
  • Wang, Y., Yu, B., Zhu, H., Liu, T., Yu, N., & Sun, L. (2021). Discontinuous named entity recognition as maximal clique discovery. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Vol. 1: Long papers) (pp. 764–774).
  • Xia, C., Zhang, C., Yang, T., Li, Y., Du, N., Wu, X., & Yu, P. (2019, July). Multi-grained named entity recognition. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 1430–1440). Association for Computational Linguistics.
  • Xu, M., & Jiang, H. (2016, November). A FOFE-based local detection approach for named entity recognition and mention detection. Computation and Language. https://doi.org/10.48550/arXiv.1611.00801.
  • Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., & Qiu, X. (2021). A unified generative framework for various NER subtasks. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Vol. 1: Long papers) (pp. 5808–5822).
  • Yu, J., Bohnet, B., & Poesio, M. (2020, July). Named entity recognition as dependency parsing. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 6470–6476). Online. Association for Computational Linguistics.
  • Yuan, Z., Tan, C., Huang, S., & Huang, F. (2022, May). Fusing heterogeneous factors with triaffine mechanism for nested named entity recognition. In Proceedings of the 60th annual meeting of the association for computational linguistics (pp. 3174–3186). Association for Computational Linguistics.
  • Zhang, N., Deng, S., Ye, H., Zhang, W., & Chen, H. (2022). Robust triple extraction with cascade bidirectional capsule network. Expert Systems with Applications, 187, 115806. https://doi.org/10.1016/j.eswa.2021.115806
  • Zhang, S., Shen, Y., Tan, Z., Wu, Y., & Lu, W. (2022, May). De-Bias for generative extraction in unified NER task. In Proceedings of the 60th annual meeting of the association for computational linguistics (Vol. 1: Long papers) (pp. 808–818). Association for Computational Linguistics.
  • Zheng, C., Cai, Y., Xu, J., Leung, H., & Xu, G. (2019). A boundary-aware neural model for nested named entity recognition. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing.
  • Zhong, X., Cambria, E., & Hussain, A. (2020). Extracting time expressions and named entities with constituent-based tagging schemes. Cognitive Computation, 12(4), 844–862. https://doi.org/10.1007/s12559-020-09714-8
  • Zhou, R., Li, X., He, R., Bing, L., Cambria, E., Si, L., & Miao, C. (2022, May). MELM: Data augmentation with masked entity language modeling for low-resource NER. In Proceedings of the 60th annual meeting of the association for computational linguistics (Vol. 1: Long papers) (pp. 2251–2262). Association for Computational Linguistics.
  • Zhu, E., & Li, J. (2022, May). Boundary smoothing for named entity recognition. In Proceedings of the 60th annual meeting of the association for computational linguistics (pp. 7096–7108). Association for Computational Linguistics.