Abstract
Relation classification is an important semantic processing task in natural language processing (NLP). Traditional works on relation classification are primarily based on supervised methods and distant supervision which rely on the large number of labels. However, these existing methods inevitably suffer from wrong labeling problem and may not perform well in resource-poor domains. We thus utilize transfer learning methods on relation classification to enable relation classification system to adapt resource-poor domains along with different relation type. In this paper, we exploit a convolutional neural network to extract lexical and syntactic features and apply transfer learning approaches for transferring the parameters of convolutional layer pre-training on general-domain corpus. The experimental results on real-world datasets demonstrate that our approach is effective and outperforms several competitive baseline methods.
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
Similar content being viewed by others
References
Mintz, M., et al.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2. Association for Computational Linguistics (2009)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Min, B., et al.: Ensemble semantics for large-scale unsupervised relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics (2012)
Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (2004)
GuoDong, Z., et al.: Exploring various knowledge in relation extraction. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (2005)
Zeng, D., et al.: Relation classification via convolutional deep neural network (2014)
Nguyen, T.H., Grishman, R.: Relation extraction: perspective from convolutional neural networks. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing (2015)
Liu, T., et al.: Neural relation extraction via inner-sentence noise reduction and transfer learning. arXiv preprint arXiv:1808.06738 (2018)
Zeng, D., et al.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (2015)
Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018)
Hendrickx, I., et al.: Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Association for Computational Linguistics (2009)
Xu, Y., et al.: Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (2015)
Gao, T., et al.: Hybrid attention-based prototypical networks for noisy few-shot relation classification (2019)
Acknowledgements
This work was supported by the National Key Research and Development Program of China (Grant no. 2016YFB1000905), the National Natural Science Foundation of China (Grant nos. 61572091, 61772096).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, Y., Zhou, Z., Li, H., Wang, G., Deng, W., Li, Z. (2020). Classifying Relation via Piecewise Convolutional Neural Networks with Transfer Learning. In: Gruca, A., Czachórski, T., Deorowicz, S., Harężlak, K., Piotrowska, A. (eds) Man-Machine Interactions 6. ICMMI 2019. Advances in Intelligent Systems and Computing, vol 1061 . Springer, Cham. https://doi.org/10.1007/978-3-030-31964-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-31964-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31963-2
Online ISBN: 978-3-030-31964-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)