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English Information

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Authors
# Name
1 Juliana Machado(julianamachado99@gmail.com)
2 Evelin Amorim(evelin.amorim@gmail.com)

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Reference
# Reference
1 Amorim, E., Campos, R., Jorge, A., Mota, P., and Almeida, R. (2024). text2story: A python toolkit to extract and visualize story components of narrative text. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15761–15772.
2 Brank, J., Leban, G., and Grobelnik, M. (2017). Annotating documents with relevant wikipedia concepts. Proceedings of SiKDD, 472.
3 Daiber, J., Jakob, M., Hokamp, C., and Mendes, P. N. (2013). Improving efficiency and accuracy in multilingual entity extraction. In Proceedings of the 9th International Conference on Semantic Systems (I-Semantics).
4 Jia, N., Cheng, X., Su, S., and Ding, L. (2021). Cogcn: Combining co-attention with graph convolutional network for entity linking with knowledge graphs. Expert Systems, 38(1):e12606.
5 Moharasan, G. and Ho, T.-B. (2019). Extraction of temporal information from clinical narratives. Journal of Healthcare Informatics Research, 3:220–244.
6 Nunes, S., Jorge, A. M., Amorim, E., Sousa, H., Leal, A., Silvano, P. M., Cantante, I., and Campos, R. (2024). Text2story lusa: A dataset for narrative analysis in european portuguese news articles. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15773–15782.
7 Santana, B., Campos, R., Amorim, E., Jorge, A., Silvano, P., and Nunes, S. (2023). A survey on narrative extraction from textual data. Artificial Intelligence Review, 56(8):8393–8435.
8 Santos, D., Mota, C., Pires, E., Langfeldt, M. C., Fuao, R. S., and Willrich, R. (2023). Dip - Desafio de identificação de personagens: objectivo, organização, recursos e resultados. Linguamática, 15(1):3–30.
9 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.
10 UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. (2013). Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations. In Second joint conference on lexical and computational semantics (* SEM), volume 2: Proceedings of the seventh international workshop on semantic evaluation (SemEval 2013), pages 1–9.
11 Wu, G., He, Y., and Hu, X. (2018). Entity linking: an issue to extract corresponding entity with knowledge base. IEEE Access, 6:6220–6231.
12 Xia, Y., Wang, X., Gu, L., Gao, Q., Jiao, J., and Wang, C. (2020). A collective entity linking algorithm with parallel computing on large-scale knowledge base. The Journal of Supercomputing, 76(2):948–963.
13 Zmandar, N., El-Haj, M., Rayson, P., Litvak, M., Giannakopoulos, G., Pittaras, N., et al. (2021). The financial narrative summarisation shared task FNS 2021. In Proceedings of the 3rd Financial Narrative Processing Workshop, pages 120–125.