The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
FLAT: Chinese NER Using Flat-Lattice Transformer
Open accessAbstract
Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information.However, since the lattice structure is complex and dynamic, most existing lattice-based models are hard to fully utilize the parallel computation of GPUs and usually have a low inference-speed.In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans.Each span corresponds to a character or latent word and its position in the original lattice.With the power of Transformer and well-designed position encoding, FLAT can fully leverage the lattice information and has an excellent parallelization ability.Experiments on four datasets show FLAT outperforms other lexicon-based models in performance and efficiency.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Neural Architectures for Named Entity Recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169
Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
Chen, Y., Xu, L., Liu, K., Zeng, D., & Zhao, J. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1017 · Cited by 906
Chinese NER Using Lattice LSTM
Zhang, Y., & Yang, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1144 · Cited by 878
Analogical Reasoning on Chinese Morphological and Semantic Relations
Li, S., Zhao, Z., Hu, R., Li, W., Liu, T., & Du, X. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2023 · Cited by 438
Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings
Peng, N., & Dredze, M. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1064 · Cited by 394
Empower Sequence Labeling with Task-Aware Neural Language Model
Liu, L., Shang, J., Ren, X., Xu, F., Gui, H., Peng, J., & Han, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12006 · Cited by 333
Cited by
Lin, T., Wang, Y., Liu, X., & Qiu, X. (2022) · AI Open · doi:10.1016/j.aiopen.2022.10.001 · Cited by 1,422
Self-citationUnified Named Entity Recognition as Word-Word Relation Classification
Li, J., Fei, H., Liu, J., Wu, S., Zhang, M., Teng, C., Ji, D., & Li, F. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21344 · Cited by 394
A Unified Generative Framework for Various NER Subtasks
Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., & Qiu, X. (2021) · 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) · doi:10.18653/v1/2021.acl-long.451 · Cited by 299
“…labelling formulation, which will assign a tag to each token in the sentence, has been widely used in the flat NER field (McCal-lum and Li, 2003; Collobert et al., 2011; Huang et al., 2015; Chiu and Nichols, 2016; Lample et al., 2016; Strakov´a et al., 2019; Yan et al., 2019; Li et al., 2020a).”
MethodSelf-citationChen, X., He, L., Hu, W., & Yi, S. (2025) · AI · doi:10.3390/ai6110290 · Cited by 2
Metadata
- Authors
- Li, X., Yan, H., Qiu, X., & Huang, X.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 6836-6842
- OpenAlex
- W3034379414
- Cited by
- 441
- References
- 23
- FWCI
- 27.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Li, X., Yan, H., Qiu, X., & Huang, X. (2020). FLAT: Chinese NER Using Flat-Lattice Transformer. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.611
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