Relation extraction in structured and unstructured data: a comparative investigation on smartphone titles in the e-commerce domain
Resumo
As large amounts of unstructured data are generated on a regular basis, expressing or storing knowledge in a way that is useful remains a challenge. In this context, Relation Extraction (RE) is the task of automatically identifying relationships in unstructured textual data. Thus, we investigated the relation extraction on unstructured e-commerce data from the smartphone domain, using a BERT model fine-tuned for this task. We conducted two experiments to acknowledge how much relational information it is possible to extract from product sheets (structured data) and product titles (unstructured data), and a third experiment to compare both. Analysis shows that extracting relations within a title can retrieve correct relations that are not evident on the related sheet.
Referências
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Hashimoto, K., Miwa, M., Tsuruoka, Y., and Chikayama, T. (2013). Simple customization of recursive neural networks for semantic relation classification. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1372–1376.
Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics.
Ji, H. and Grishman, R. (2006). Analysis and repair of name tagger errors. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 420–427.
Li, Y., Jiang, J., Chieu, H. L., and Chai, K. M. A. (2011). Extracting relation descriptors with conditional random fields. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 392–400.
Information extraction: algorithms and prospects in a retrieval Moens, M.-F. (2006). context, volume 21. Springer Science & Business Media.
Pawar, S., Palshikar, G. K., and Bhattacharyya, P. (2017). Relation extraction: A survey. arXiv preprint arXiv:1712.05191.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and ZettlearXiv preprint moyer, L. (2018). Deep contextualized word representations. arXiv:1802.05365.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding with unsupervised learning.
Sarawagi, S. (2008). Information extraction. Found. Trends Databases, 1(3):261–377.
Soares, L. B., FitzGerald, N., Ling, J., and Kwiatkowski, T. (2019). Matching the blanks: Distributional similarity for relation learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2895–2905, Florence, Italy. Association for Computational Linguistics.
Socher, R., Huval, B., Manning, C. D., and Ng, A. Y. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pages 1201–1211.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K. (2020). Product knowledge graph embedding for e-commerce. In Proceedings of the 13th International Conference on Web Search and Data Mining, WSDM ’20, page 672–680, New York, NY, USA. Association for Computing Machinery.
Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014). Relation classification via convolutional deep neural network. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pages 2335–2344.
Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C. D. (2017). Position-aware attention and supervised data improve slot filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 35–45.
Zitouni, I. and Florian, R. (2008). Mention detection crossing the language barrier. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 600–609.
Doddington, G. R., Mitchell, A., Przybocki, M. A., Ramshaw, L. A., Strassel, S. M., and Weischedel, R. M. (2004). The automatic content extraction (ace) program-tasks, data, and evaluation. In Lrec, volume 2, pages 837–840. Lisbon.
Hashimoto, K., Miwa, M., Tsuruoka, Y., and Chikayama, T. (2013). Simple customization of recursive neural networks for semantic relation classification. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1372–1376.
Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics.
Ji, H. and Grishman, R. (2006). Analysis and repair of name tagger errors. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 420–427.
Li, Y., Jiang, J., Chieu, H. L., and Chai, K. M. A. (2011). Extracting relation descriptors with conditional random fields. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 392–400.
Information extraction: algorithms and prospects in a retrieval Moens, M.-F. (2006). context, volume 21. Springer Science & Business Media.
Pawar, S., Palshikar, G. K., and Bhattacharyya, P. (2017). Relation extraction: A survey. arXiv preprint arXiv:1712.05191.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and ZettlearXiv preprint moyer, L. (2018). Deep contextualized word representations. arXiv:1802.05365.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding with unsupervised learning.
Sarawagi, S. (2008). Information extraction. Found. Trends Databases, 1(3):261–377.
Soares, L. B., FitzGerald, N., Ling, J., and Kwiatkowski, T. (2019). Matching the blanks: Distributional similarity for relation learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2895–2905, Florence, Italy. Association for Computational Linguistics.
Socher, R., Huval, B., Manning, C. D., and Ng, A. Y. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pages 1201–1211.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K. (2020). Product knowledge graph embedding for e-commerce. In Proceedings of the 13th International Conference on Web Search and Data Mining, WSDM ’20, page 672–680, New York, NY, USA. Association for Computing Machinery.
Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014). Relation classification via convolutional deep neural network. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pages 2335–2344.
Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C. D. (2017). Position-aware attention and supervised data improve slot filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 35–45.
Zitouni, I. and Florian, R. (2008). Mention detection crossing the language barrier. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 600–609.
Publicado
29/11/2021
Como Citar
BARBIRATO, João Gabriel Melo; REAL, Livy; CASELI, Helena de Medeiros.
Relation extraction in structured and unstructured data: a comparative investigation on smartphone titles in the e-commerce domain. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 101-110.
DOI: https://doi.org/10.5753/stil.2021.17789.
