Abstract
Relation extraction between two named entities from unstructured text is an important natural language processing task. In the absence of labelled data, semi-supervised and unsupervised approaches are used to extract relations. We present a novel approach that uses sentence encoding for unsupervised relation extraction. We use a pre-trained, SBERT based model for sentence encoding. Our approach classifies identical sentences using a clustering algorithm. These sentences are used to extract relations between two named entities in a given text. The system calculates a confidence value above a certain threshold to avoid semantic drift. The experimental results show that without any explicit feature selection and independent of the size of the corpus, our proposed approach achieves a better F-score than state-of-the-art unsupervised models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: Proceedings of the Fifth ACM Conference on Digital Libraries. DL 2000, New York, NY, USA, pp. 85–94. Association for Computing Machinery (2000). https://doi.org/10.1145/336597.336644
Batista, D.S., Martins, B., Silva, M.J.: Semi-supervised bootstrapping of relationship extractors with distributional semantics. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 499–504 (2015)
Blank, A.: Why do new meanings occur? A cognitive typology of the motivations for lexical semantic change, pp. 61–90. De Gruyter Mouton (2013). https://doi.org/10.1515/9783110804195.61
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)
Elsahar, H., Demidova, E., Gottschalk, S., Gravier, C., Laforest, F.: Unsupervised open relation extraction. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10577, pp. 12–16. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70407-4_3
Marcheggiani, D., Titov, I.: Discrete-state variational autoencoders for joint discovery and factorization of relations. TACL 4, 231–244 (2016). https://www.aclweb.org/anthology/Q16-1017
Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)
Simon, É., Guigue, V., Piwowarski, B.: Unsupervised information extraction: Regularizing discriminative approaches with relation distribution losses. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1378–1387. Association for Computational Linguistics, Florence, Italy (Jul 2019). https://doi.org/10.18653/v1/P19-1133, https://www.aclweb.org/anthology/P19-1133
Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, pp. 455–465. Association for Computational Linguistics (2012). https://www.aclweb.org/anthology/D12-1042
Tran, T.T., Le, P., Ananiadou, S.: Revisiting unsupervised relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7498–7505. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.669
Acknowledgement
This work has been supported by the EU H2020 Marie Skłodowska-Curie project KnowGraphs (860801), the BMBF-funded EuroStars projects E!113314 FROCKG (01QE19418) and E! 114154 PORQUE (01QE2056C).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ali, M., Saleem, M., Ngomo, AC.N. (2021). Unsupervised Relation Extraction Using Sentence Encoding. In: Verborgh, R., et al. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science(), vol 12739. Springer, Cham. https://doi.org/10.1007/978-3-030-80418-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-80418-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80417-6
Online ISBN: 978-3-030-80418-3
eBook Packages: Computer ScienceComputer Science (R0)