Identification of Participants of Narratives Using Knowledge Bases
Resumo
Identifying participants in narratives is important to understand and extract meaning from unstructured texts. This paper investigates the use of DBpedia and Wikifier for this task. We tested these two knowledge base platforms to evaluate their performance in recognizing and extracting entities in Portuguese-language journalistic narrative texts. The results show that both DBpedia and Wikifier present similar results in identifying participants, around 0.40 in the f1-score. The objective of this paper is to study the potential of knowledge bases to improve the understanding of narratives, in addition to suggesting directions for future research in this domain.
Palavras-chave:
Natural Language Processing, Entity Linking, Knowledge Bases
Referências
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.
Brank, J., Leban, G., and Grobelnik, M. (2017). Annotating documents with relevant wikipedia concepts. Proceedings of SiKDD, 472.
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).
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.
Moharasan, G. and Ho, T.-B. (2019). Extraction of temporal information from clinical narratives. Journal of Healthcare Informatics Research, 3:220–244.
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.
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.
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.
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.
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.
Wu, G., He, Y., and Hu, X. (2018). Entity linking: an issue to extract corresponding entity with knowledge base. IEEE Access, 6:6220–6231.
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.
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.
Brank, J., Leban, G., and Grobelnik, M. (2017). Annotating documents with relevant wikipedia concepts. Proceedings of SiKDD, 472.
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).
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.
Moharasan, G. and Ho, T.-B. (2019). Extraction of temporal information from clinical narratives. Journal of Healthcare Informatics Research, 3:220–244.
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.
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.
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.
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.
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.
Wu, G., He, Y., and Hu, X. (2018). Entity linking: an issue to extract corresponding entity with knowledge base. IEEE Access, 6:6220–6231.
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.
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.
Publicado
14/10/2024
Como Citar
MACHADO, Juliana; AMORIM, Evelin.
Identification of Participants of Narratives Using Knowledge Bases. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 771-777.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.243103.