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Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially.
Alsentzer, E., Murphy, J., Boag, W., Weng, W.H., Jindi, D., Naumann, T., McDermott, M.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop. pp. 72–78. Association for Computational Linguistics, Minneapolis, Minnesota, USA (Jun 2019). https://doi.org/10.18653/v1/W19-1909, https://aclanthology.org/W19-1909
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Sun, A., Grishman, R., Sekine, S.: Semi-supervised relation extraction with large-scale word clustering. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. pp. 521–529. Association for Computational Linguistics, Portland, Oregon, USA (Jun 2011), https://aclanthology.org/P11-1053
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Gurulingappa, H., Rajput, A.M., Roberts, A., Fluck, J., Hofmann-Apitius, M., Toldo, L.: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of Biomedical Informatics 45
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Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. pp. 66–71. Association for Computational Linguistics, Brussels, Belgium (Nov 2018). https://doi.org/10.18653/v1/D18-2012, https://aclanthology.org/D18-2012
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Plank, B., Moschitti, A.: Embedding semantic similarity in tree kernels for domain adaptation of relation extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1498–1507. Association for Computational Linguistics, Sofia, Bulgaria (Aug 2013), https://aclanthology.org/P13-1147
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Johnson, A.E., Pollard, T.J., Shen, L., Lehman, L.w.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., Mark, R.G.: Mimic-iii, a freely accessible critical care database. Scientific data 3
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Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1715–1725. Association for Computational Linguistics, Berlin, Germany (Aug 2016). https://doi.org/10.18653/v1/P16-1162, https://aclanthology.org/P16-1162
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Li, F., Zhang, M., Fu, G., Ji, D.: A neural joint model for entity and relation extraction from biomedical text. BMC bioinformatics 18
2017
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Bekoulis, G., Deleu, J., Demeester, T., Develder, C.: Joint entity recognition and relation extraction as a multi-head selection problem. Expert Systems with Applications 114
2018
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Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). pp. 2227–2237. Association for Computational Linguistics, New Orleans, Louisiana (Jun 2018). https://doi.org/10.18653/v1/N18-1202, https://aclanthology.org/N18-1202
2018
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Wang, J., Lu, W.: Two are better than one: Joint entity and relation extraction with table-sequence encoders. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 1706–1721. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.emnlp-main.133, https://aclanthology.org/2020.emnlp-main.133
2020
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Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., Rush, A.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. pp. 38–45. Association for Computational Linguistics, Online (Oct 2020). https://doi.org/10.18653/v1/2020.emnlp-demos.6, https://aclanthology.org/2020.emnlp-demos.6
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: 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, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
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Antoun, W., Baly, F., Hajj, H.: AraBERT: Transformer-based model for Arabic language understanding. In: Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection. pp. 9–15. European Language Resource Association, Marseille, France (May 2020), https://aclanthology.org/2020.osact-1.2
2020
Cited alongside, same era.
Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. In: ECAI 2020, pp. 2006–2013. IOS Press, Online (2020). https://doi.org/10.3233/FAIA200321
2020
Cited alongside, same era.
El Boukkouri, H., Ferret, O., Lavergne, T., Noji, H., Zweigenbaum, P., Tsujii, J.: CharacterBERT: Reconciling ELMo and BERT for word-level open-vocabulary representations from characters. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 6903–6915. International Committee on Computational Linguistics, Barcelona, Spain (Online) (Dec 2020). https://doi.org/10.18653/v1/2020.coling-main.609, https://aclanthology.org/2020.coling-main.609
2020
Cited alongside, same era.
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics (Oxford, England) 36
2020
Cited alongside, same era.
Taillé, B., Guigue, V., Scoutheeten, G., Gallinari, P.: Let’s stop error propagation in the end-to-end relation extraction literature! In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). pp. 3689–3701. Association for Computational Linguistics, Online (2020), https://www.aclweb.org/anthology/2020.emnlp-main.301.pdf
2020
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2020
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Ács, J., Kádár, Á., Kornai, A.: Subword pooling makes a difference. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. pp. 2284–2295. Association for Computational Linguistics, Online (Apr 2021). https://doi.org/10.18653/v1/2021.eacl-main.194, https://aclanthology.org/2021.eacl-main.194
2021
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2021
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2021
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2021
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Theodoropoulos, C., Henderson, J., Coman, A.C., Moens, M.F.: Imposing relation structure in language-model embeddings using contrastive learning. In: Proceedings of the 25th Conference on Computational Natural Language Learning. pp. 337–348. Association for Computational Linguistics, Online (Nov 2021). https://doi.org/10.18653/v1/2021.conll-1.27, https://aclanthology.org/2021.conll-1.27
2021
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Yan, Z., Zhang, C., Fu, J., Zhang, Q., Wei, Z.: A partition filter network for joint entity and relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 185–197. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.emnlp-main.17, https://aclanthology.org/2021.emnlp-main.17
2021
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Zhang, H., Tan, L.: Textual representations for crosslingual information retrieval. In: Proceedings of The 4th Workshop on e-Commerce and NLP. pp. 116–122 (2021), https://aclanthology.org/2021.ecnlp-1.14.pdf
2021
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Clark, J.H., Garrette, D., Turc, I., Wieting, J.: Canine: Pre-training an efficient tokenization-free encoder for language representation. Transactions of the Association for Computational Linguistics 10
2022
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Xue, L., Barua, A., Constant, N., Al-Rfou, R., Narang, S., Kale, M., Roberts, A., Raffel, C.: ByT5: Towards a token-free future with pre-trained byte-to-byte models. Transactions of the Association for Computational Linguistics 10
2022
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Sasaki, S., Sun, S., Schamoni, S., Duh, K., Inui, K.: Cross-lingual learning-to-rank with shared representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). pp. 458–463. Association for Computational Linguistics, New Orleans, Louisiana (Jun 2018). https://doi.org/10.18653/v1/N18-2073, https://aclanthology.org/N18-2073
2073
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