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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-030-80418-3_25
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