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English Information

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Authors
# Name
1 Daniel Lucas Albuquerque(daniellucasfreitas@gmail.com)
2 Vitória Santos(vit2santoss@gmail.com)
3 Pedro Nack(pedro.nackm@gmail.com)
4 Renato Fileto(r.fileto@ufsc.br)
5 Carina Dorneles(carina.dorneles@ufsc.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
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2 Barba, E., Procopio, L., and Navigli, R. (2022). Extend: Extractive entity disambiguation. In Proc.of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2478–2488.
3 Besta, M., Barth, J., Schreiber, E., Kubicek, A., Catarino, A., Gerstenberger, R., Ny- czyk, P., Iff, P., Li, Y., Houliston, S., Sternal, T., Copik, M., Kwaśniewski, G., Müller, J., Łukasz Flis, Eberhard, H., Niewiadomski, H., and Hoefler, T. (2025). Reasoning language models: A blueprint.
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9 Jurafsky, D. and Martin, J. H. (2024). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Third edition draft edition.
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12 Liu, X., Liu, Y., Zhang, K., Wang, K., Liu, Q., and Chen, E. (2024). Onenet: A fine- tuning free framework for few-shot entity linking via large language model prompting. arXiv preprint arXiv:2410.07549.
13 Miranda, N., Machado, M. M., and Moreira, D. A. (2024). Ontodrug: Enhancing brazilian health system interoperability with a national medication ontology. In Brazilian Symposium on Multimedia and the Web (WebMedia), pages 240–248. SBC.
14 Nascimento, E. and Casanova, M. A. (2024). Querying databases with natural language: The use of large language models for text-to-sql tasks. In Anais Estendidos do XXXIX Simp. Brasileiro de Bancos de Dados, pages 196–201, Porto Alegre, RS, Brasil. SBC.
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18 Romero, P., Han, L., and Nenadic, G. (2025). Medication extraction and entity linking using stacked and voted ensembles on LLMs. In Ananiadou, S., Demner-Fushman, D., Gupta, D., and Thompson, P., editors, Proceedings of the Second Workshop on Patient- Oriented Language Processing (CL4Health), pages 303–315, Albuquerque, New Mex- ico. Association for Computational Linguistics.
19 Santos, V. and Dorneles, C. (2024). Unveiling the segmentation power of llms: Zero-shot invoice item description analysis. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 549–561, Porto Alegre, RS, Brasil. SBC.
20 Sevgili, Ö., Shelmanov, A., Arkhipov, M., Panchenko, A., and Biemann, C. (2022). Neural entity linking: A survey of models based on deep learning. Semantic Web, 13(3):527–570.
21 Shen, W., Li, Y., Liu, Y., Han, J., Wang, J., and Yuan, X. (2023). Entity linking meets deep learning: Techniques and solutions. IEEE Transactions on Knowledge and Data Engineering, 35(3):2556–2578.
22 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
23 Trivedi, H., Balasubramanian, N., Khot, T., and Sabharwal, A. (2023). Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions.
24 Vollmers, D., Zahera, H., Moussallem, D., and Ngomo, A.-C. N. (2025). Contextual augmentation for entity linking using large language models. In Proc.of the 31st International Conference on Computational Linguistics, pages 8535–8545.
25 Wang, S., Li, A. H., Zhu, H., Zhang, S., Hang, C.-W., Perera, P., Ma, J., Wang, W., Wang, Z., Castelli, V., et al. (2023). Benchmarking diverse-modal entity linking with generative models. arXiv preprint arXiv:2305.17337.
26 Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA. Curran Associates Inc.
27 Xiao, Z., Gong, M., Wu, J., Zhang, X., Shou, L., Pei, J., and Jiang, D. (2023). Instructed language models with retrievers are powerful entity linkers. arXiv preprint arXiv:2311.03250.
28 Xin, A., Qi, Y., Yao, Z., Zhu, F., Zeng, K., Bin, X., Hou, L., and Li, J. (2024). Llmael: Large language models are good context augmenters for entity linking. arXiv preprint arXiv:2407.04020.