Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query

Author:

Shi Kunli,Chen Gongchi,Gu Jinghang,Qian Longhua,Zhou Guodong

Publisher

Springer Nature Singapore

Reference24 articles.

1. Mayhew, S., Tsai, C.-T., Roth, D.: Cheap translation for cross-lingual named entity recognition. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2536–2545. Association for Computational Linguistics, Copenhagen (2017). https://doi.org/10.18653/v1/D17-1269

2. Xie, J., Yang, Z., Neubig, G., Smith, N.A., Carbonell, J.: Neural cross-lingual named entity recognition with minimal resources (2018). http://arxiv.org/abs/1808.09861

3. Wu, S., Dredze, M.: Beto, bentz, becas: the surprising cross-lingual effectiveness of BERT. 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. 833–844. Association for Computational Linguistics, Hong Kong (2019). https://doi.org/10.18653/v1/D19-1077

4. Tsai, C.-T., Mayhew, S., Roth, D.: Cross-lingual named entity recognition via wikification. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, pp. 219–228. Association for Computational Linguistics, Berlin (2016). https://doi.org/10.18653/v1/K16-1022

5. Keung, P., Lu, Y., Bhardwaj, V.: Adversarial learning with contextual embeddings for zero-resource cross-lingual classification and 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. 1355–1360. Association for Computational Linguistics, Hong Kong (2019). https://doi.org/10.18653/v1/D19-1138

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