Paper Information | SBC BOOKS

#	Name
1	Pedro Azevedo(pedro.henrique.azevedo@posgrad.ufsc.br)
2	Renato Fileto(r.fileto@ufsc.br)
3	Andre Zibetti(andre.zibetti@ufsc.br)
4	SImone Werner(simone.werner@ufsc.br)

#	Reference
1	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. In Al-Onaizan, Y., Bansal, M., and Chen, Y.-N., editors, Proceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 13634–13651, Miami, Florida, USA. Association for Computational Linguistics.
2	Albuquerque, D. L., Santos, V., Nack, P., Fileto, R., and Dorneles, C. (2025). Language models are not a panacea: Combining them with domain knowledge and efficient indexes for entity linking. In Simp. Brasileiro de Bancos de Dados (SBBD), pages 479–492, Porto Alegre, RS, Brasil. SBC.
3	Romero, P., Han, L., and Nenadic, G. (2025). INSIGHTBUDDY-AI: Medication extraction and entity linking using pre-trained language models and ensemble learning. In Ebrahimi, A., Haider, S., Liu, E., Haider, S., Leonor Pacheco, M., and Wein, S., editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 18–27, Albuquerque, USA. Association for Computational Linguistics.
4	Ayoola, T., Tyagi, S., Fisher, J., Christodoulopoulos, C., and Pierleoni, A. (2022). ReFinED: An efficient zero-shot-capable approach to end-to-end entity linking. In Loukina, A., Gangad- haraiah, R., and Min, B., editors, Conf. of the North American Chapter of the ACL: Human Language Technologies: Industry Track, pages 209–220, Hybrid: Seattle, Washington + On- line. Association for Computational Linguistics (ACL).
5	Rynkiewicz, A. A., Palma, R., and Formanowicz, P. (2025). Universal entity linking. Engineering Applications of Artificial Intelligence, 161:112185.
6	Balog, K. (2018). Entity-Oriented Search, volume 39 of The Information Retrieval Series. Springer International Publishing.
7	Shen, W., Wang, J., and Han, J. (2015). Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2):443–460.
8	Ding, Y., Poudel, A., Zeng, Q., Weninger, T., Veeramani, B., and Bhattacharya, S. (2025). Entgpt: Entity linking with generative large language models. arXiv preprint.
9	Shlyk, D., Groza, T., Mesiti, M., Montanelli, S., and Cavalleri, E. (2024). REAL: A retrieval- augmented entity linking approach for biomedical concept recognition. In Demner-Fushman, D., Ananiadou, S., Miwa, M., Roberts, K., and Tsujii, J., editors, Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 380–389, Bangkok, Thailand. Association for Computational Linguistics.
10	Heisler, G., Beckhauser, W., Santos, V., and Fileto, R. (2025). Detection of vehicle purchases in various invoices using large-scale language models. In Anais da I Escola Regional de Apren- dizado de M´aquina e Inteligência Artificial da Região Sul, pages 164–167, Porto Alegre, RS, Brasil. SBC
11	Tedeschi, S., Conia, S., Cecconi, F., and Navigli, R. (2021). Named Entity Recognition for Entity Linking: What works and what’s next. In Moens, M.-F., Huang, X., Specia, L., and Yih, S. W.-t., editors, Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2584–2596, Punta Cana, Dominican Republic. Association for Computational Linguistics.
12	Li, Y., Galimov, A., Ganapaneni, M. D., Thejaswi, P., Meng, D., Kumar, P., and Potdar, S. (2025). Leveraging the power of large language models in entity linking via adaptive routing and tar- geted reasoning. In Potdar, S., Rojas-Barahona, L., and Montella, S., editors, Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 871–882, Suzhou (China). Association for Computational Linguistics.
13	Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). Llama: Open and efficient foundation language models.
14	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. In Al-Onaizan, Y., Bansal, M., and Chen, Y.-N., editors, Proceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 13634–13651, Miami, Florida, USA. Association for Computational Linguistics.
15	Vollmers, D., Zahera, H., Moussallem, D., and Ngonga Ngomo, A.-C. (2025). Contextual aug- mentation for entity linking using large language models. In Rambow, O., Wanner, L., Apidi- anaki, M., Al-Khalifa, H., Eugenio, B. D., and Schockaert, S., editors, Proceedings of the 31st International Conference on Computational Linguistics, pages 8535–8545, Abu Dhabi, UAE. Association for Computational Linguistics.
16	Romero, P., Han, L., and Nenadic, G. (2025). INSIGHTBUDDY-AI: Medication extraction and entity linking using pre-trained language models and ensemble learning. In Ebrahimi, A., Haider, S., Liu, E., Haider, S., Leonor Pacheco, M., and Wein, S., editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 18–27, Albuquerque, USA. Association for Computational Linguistics.
17	Wang, F., Tao, Z., Wang, M., Hu, M., and Bai, X. (2025). AELC: Adaptive entity linking with LLM-driven contextualization. In Christodoulopoulos, C., Chakraborty, T., Rose, C., and Peng, V., editors, Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4313–4327, Suzhou, China. Association for Computational Linguistics.
18	Rynkiewicz, A. A., Palma, R., and Formanowicz, P. (2025). Universal entity linking. Engineering Applications of Artificial Intelligence, 161:112185.
19	Shen, W., Wang, J., and Han, J. (2015). Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2):443–460.
20	Shlyk, D., Groza, T., Mesiti, M., Montanelli, S., and Cavalleri, E. (2024). REAL: A retrieval- augmented entity linking approach for biomedical concept recognition. In Demner-Fushman, D., Ananiadou, S., Miwa, M., Roberts, K., and Tsujii, J., editors, Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 380–389, Bangkok, Thailand. Association for Computational Linguistics.
21	Xin, A., Qi, Y., Yao, Z., Zhu, F., Zeng, K., Xu, B., Hou, L., and Li, J. (2025). Llmael: Large language models are good context augmenters for entity linking. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM ’25, page 3550–3559, New York, NY, USA. Association for Computing Machinery.
22	Tedeschi, S., Conia, S., Cecconi, F., and Navigli, R. (2021). Named Entity Recognition for Entity Linking: What works and what’s next. In Moens, M.-F., Huang, X., Specia, L., and Yih, S. W.-t., editors, Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2584–2596, Punta Cana, Dominican Republic. Association for Computational Linguistics.
23	Ye, C. and Mitchell, C. S. (2025). LLM as entity disambiguator for biomedical entity-linking. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 301–312, Vienna, Austria. Association for Computational Linguistics.
24	Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). Llama: Open and efficient foundation language models.
25	Zhou, D., Sch¨arli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E. (2023). Least-to-most prompting enables complex reasoning in large language models.
26	Vollmers, D., Zahera, H., Moussallem, D., and Ngonga Ngomo, A.-C. (2025). Contextual aug- mentation for entity linking using large language models. In Rambow, O., Wanner, L., Apidi- anaki, M., Al-Khalifa, H., Eugenio, B. D., and Schockaert, S., editors, Proceedings of the 31st International Conference on Computational Linguistics, pages 8535–8545, Abu Dhabi, UAE. Association for Computational Linguistics.
27	Zhou, K., Li, Y., Wang, Q., Qiao, Q., and Li, Q. (2024). GenDecider: Integrating “none of the candidates” judgments in zero-shot entity linking re-ranking. In Duh, K., Gomez, H., and Bethard, S., editors, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 239–245, Mexico City, Mexico. Association for Computational Linguistics.
28	Wang, F., Tao, Z., Wang, M., Hu, M., and Bai, X. (2025). AELC: Adaptive entity linking with LLM-driven contextualization. In Christodoulopoulos, C., Chakraborty, T., Rose, C., and Peng, V., editors, Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4313–4327, Suzhou, China. Association for Computational Linguistics.
29	Xin, A., Qi, Y., Yao, Z., Zhu, F., Zeng, K., Xu, B., Hou, L., and Li, J. (2025). Llmael: Large language models are good context augmenters for entity linking. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM ’25, page 3550–3559, New York, NY, USA. Association for Computing Machinery.
30	Ye, C. and Mitchell, C. S. (2025). LLM as entity disambiguator for biomedical entity-linking. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 301–312, Vienna, Austria. Association for Computational Linguistics.
31	Zhou, D., Sch¨arli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E. (2023). Least-to-most prompting enables complex reasoning in large language models.
32	Zhou, K., Li, Y., Wang, Q., Qiao, Q., and Li, Q. (2024). GenDecider: Integrating “none of the candidates” judgments in zero-shot entity linking re-ranking. In Duh, K., Gomez, H., and Bethard, S., editors, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 239–245, Mexico City, Mexico. Association for Computational Linguistics.

1

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. In Al-Onaizan, Y., Bansal, M., and Chen, Y.-N., editors, Proceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 13634–13651, Miami, Florida, USA. Association for Computational Linguistics.

2

Albuquerque, D. L., Santos, V., Nack, P., Fileto, R., and Dorneles, C. (2025). Language models are not a panacea: Combining them with domain knowledge and efficient indexes for entity linking. In Simp. Brasileiro de Bancos de Dados (SBBD), pages 479–492, Porto Alegre, RS, Brasil. SBC.

3

Romero, P., Han, L., and Nenadic, G. (2025). INSIGHTBUDDY-AI: Medication extraction and entity linking using pre-trained language models and ensemble learning. In Ebrahimi, A., Haider, S., Liu, E., Haider, S., Leonor Pacheco, M., and Wein, S., editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 18–27, Albuquerque, USA. Association for Computational Linguistics.

4

Ayoola, T., Tyagi, S., Fisher, J., Christodoulopoulos, C., and Pierleoni, A. (2022). ReFinED: An efficient zero-shot-capable approach to end-to-end entity linking. In Loukina, A., Gangad- haraiah, R., and Min, B., editors, Conf. of the North American Chapter of the ACL: Human Language Technologies: Industry Track, pages 209–220, Hybrid: Seattle, Washington + On- line. Association for Computational Linguistics (ACL).

5

Rynkiewicz, A. A., Palma, R., and Formanowicz, P. (2025). Universal entity linking. Engineering Applications of Artificial Intelligence, 161:112185.

6

Balog, K. (2018). Entity-Oriented Search, volume 39 of The Information Retrieval Series. Springer International Publishing.

7

Shen, W., Wang, J., and Han, J. (2015). Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2):443–460.

8

Ding, Y., Poudel, A., Zeng, Q., Weninger, T., Veeramani, B., and Bhattacharya, S. (2025). Entgpt: Entity linking with generative large language models. arXiv preprint.

9

Shlyk, D., Groza, T., Mesiti, M., Montanelli, S., and Cavalleri, E. (2024). REAL: A retrieval- augmented entity linking approach for biomedical concept recognition. In Demner-Fushman, D., Ananiadou, S., Miwa, M., Roberts, K., and Tsujii, J., editors, Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 380–389, Bangkok, Thailand. Association for Computational Linguistics.

10

Heisler, G., Beckhauser, W., Santos, V., and Fileto, R. (2025). Detection of vehicle purchases in various invoices using large-scale language models. In Anais da I Escola Regional de Apren- dizado de M´aquina e Inteligência Artificial da Região Sul, pages 164–167, Porto Alegre, RS, Brasil. SBC

11

Tedeschi, S., Conia, S., Cecconi, F., and Navigli, R. (2021). Named Entity Recognition for Entity Linking: What works and what’s next. In Moens, M.-F., Huang, X., Specia, L., and Yih, S. W.-t., editors, Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2584–2596, Punta Cana, Dominican Republic. Association for Computational Linguistics.

12

Li, Y., Galimov, A., Ganapaneni, M. D., Thejaswi, P., Meng, D., Kumar, P., and Potdar, S. (2025). Leveraging the power of large language models in entity linking via adaptive routing and tar- geted reasoning. In Potdar, S., Rojas-Barahona, L., and Montella, S., editors, Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 871–882, Suzhou (China). Association for Computational Linguistics.

13

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). Llama: Open and efficient foundation language models.

14

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. In Al-Onaizan, Y., Bansal, M., and Chen, Y.-N., editors, Proceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 13634–13651, Miami, Florida, USA. Association for Computational Linguistics.

15

Vollmers, D., Zahera, H., Moussallem, D., and Ngonga Ngomo, A.-C. (2025). Contextual aug- mentation for entity linking using large language models. In Rambow, O., Wanner, L., Apidi- anaki, M., Al-Khalifa, H., Eugenio, B. D., and Schockaert, S., editors, Proceedings of the 31st International Conference on Computational Linguistics, pages 8535–8545, Abu Dhabi, UAE. Association for Computational Linguistics.

16

Romero, P., Han, L., and Nenadic, G. (2025). INSIGHTBUDDY-AI: Medication extraction and entity linking using pre-trained language models and ensemble learning. In Ebrahimi, A., Haider, S., Liu, E., Haider, S., Leonor Pacheco, M., and Wein, S., editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 18–27, Albuquerque, USA. Association for Computational Linguistics.

17

Wang, F., Tao, Z., Wang, M., Hu, M., and Bai, X. (2025). AELC: Adaptive entity linking with LLM-driven contextualization. In Christodoulopoulos, C., Chakraborty, T., Rose, C., and Peng, V., editors, Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4313–4327, Suzhou, China. Association for Computational Linguistics.

18

Rynkiewicz, A. A., Palma, R., and Formanowicz, P. (2025). Universal entity linking. Engineering Applications of Artificial Intelligence, 161:112185.

19

Shen, W., Wang, J., and Han, J. (2015). Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2):443–460.

20

Shlyk, D., Groza, T., Mesiti, M., Montanelli, S., and Cavalleri, E. (2024). REAL: A retrieval- augmented entity linking approach for biomedical concept recognition. In Demner-Fushman, D., Ananiadou, S., Miwa, M., Roberts, K., and Tsujii, J., editors, Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 380–389, Bangkok, Thailand. Association for Computational Linguistics.

21

Xin, A., Qi, Y., Yao, Z., Zhu, F., Zeng, K., Xu, B., Hou, L., and Li, J. (2025). Llmael: Large language models are good context augmenters for entity linking. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM ’25, page 3550–3559, New York, NY, USA. Association for Computing Machinery.

22

Tedeschi, S., Conia, S., Cecconi, F., and Navigli, R. (2021). Named Entity Recognition for Entity Linking: What works and what’s next. In Moens, M.-F., Huang, X., Specia, L., and Yih, S. W.-t., editors, Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2584–2596, Punta Cana, Dominican Republic. Association for Computational Linguistics.

23

Ye, C. and Mitchell, C. S. (2025). LLM as entity disambiguator for biomedical entity-linking. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 301–312, Vienna, Austria. Association for Computational Linguistics.

24

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). Llama: Open and efficient foundation language models.

25

Zhou, D., Sch¨arli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E. (2023). Least-to-most prompting enables complex reasoning in large language models.

26

Vollmers, D., Zahera, H., Moussallem, D., and Ngonga Ngomo, A.-C. (2025). Contextual aug- mentation for entity linking using large language models. In Rambow, O., Wanner, L., Apidi- anaki, M., Al-Khalifa, H., Eugenio, B. D., and Schockaert, S., editors, Proceedings of the 31st International Conference on Computational Linguistics, pages 8535–8545, Abu Dhabi, UAE. Association for Computational Linguistics.

27

Zhou, K., Li, Y., Wang, Q., Qiao, Q., and Li, Q. (2024). GenDecider: Integrating “none of the candidates” judgments in zero-shot entity linking re-ranking. In Duh, K., Gomez, H., and Bethard, S., editors, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 239–245, Mexico City, Mexico. Association for Computational Linguistics.

28

Wang, F., Tao, Z., Wang, M., Hu, M., and Bai, X. (2025). AELC: Adaptive entity linking with LLM-driven contextualization. In Christodoulopoulos, C., Chakraborty, T., Rose, C., and Peng, V., editors, Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4313–4327, Suzhou, China. Association for Computational Linguistics.

29

Xin, A., Qi, Y., Yao, Z., Zhu, F., Zeng, K., Xu, B., Hou, L., and Li, J. (2025). Llmael: Large language models are good context augmenters for entity linking. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM ’25, page 3550–3559, New York, NY, USA. Association for Computing Machinery.

30

Ye, C. and Mitchell, C. S. (2025). LLM as entity disambiguator for biomedical entity-linking. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 301–312, Vienna, Austria. Association for Computational Linguistics.

31

Zhou, D., Sch¨arli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E. (2023). Least-to-most prompting enables complex reasoning in large language models.

32

Zhou, K., Li, Y., Wang, Q., Qiao, Q., and Li, Q. (2024). GenDecider: Integrating “none of the candidates” judgments in zero-shot entity linking re-ranking. In Duh, K., Gomez, H., and Bethard, S., editors, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 239–245, Mexico City, Mexico. Association for Computational Linguistics.

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