Abstract
Semantic role labeling task is a way of shallow semantic analysis. Its research results are of great significance for promoting Machine Translation [1], Question Answering [2], Human Robot Interaction [3] and other application systems. The goal of semantic role labeling is to recover the predicate-argument structure of a sentence, based on the sentences entered and the predicates specified in the sentence. Then mark the relationship between the predicate and the argument, such as time, place, the agent, the victim, and so on. This paper introduces the main research directions of semantic role labeling and the research status at home and abroad in recent years. And summarized a large number of research results based on statistical machine learning and deep neural networks. The main purpose is to analyze the method of semantic role labeling and its current status. Summarize the development trend of the future semantic role labeling.
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
Knight, K., Luk, S.K.: Building a large-scale knowledge base for machine translation. Comput. Sci. 773–778 (1994)
Shen, D., Lapata, M.: Using semantic roles to improve question answering. EMNLP-CoNLL 2007. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 12–21, June 28–30, 2007, Prague, Czech Republic. DBLP (2007)
Bastianelli, E., Castellucci, G., Croce, D., et al.: Textual inference and meaning representation in human robot interaction. Newdesign.aclweb.org (2013)
Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Comput. Linguist. 28(28), 245–288 (2002)
Liu, T., Che, W., Li, S., et al.: Semantic role labeling system using maximum entropy classifier. In: Conference on Computational Natural Language Learning. Association for Computational Linguistics, pp. 189–192 (2005)
Liu, H., Che, W., Liu, T.: Feature engineering for Chinese semantic role labeling. J. Chin. Inf. Process. 21(1), 79–84 (2007)
Li, J., Zhou, G., Zhu, M., et al.: Semantic role labeling of nominalized predicates in Chinese. J. Softw. 22(8), 1725–1737 (2011)
Collobert, R., Weston, J., Karlen, M., et al.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(1), 2493–2537 (2011)
Zhou, J., Xu, W.: End-to-end learning of semantic role labeling using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 1: Long Papers), vol. 1, pp. 1127–1137 (2015)
Graves, A., Liwicki, M., Fernã n S, et al.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)
He, L., Lee, K., Lewis, M., et al.: Deep semantic role labeling: what works and what’s next. In: Meeting of the Association for Computational Linguistics, pp. 473–483 (2017)
Wang, M., Liu, Q.: Semantic role labeling using deep neural networks. J. Chin. Inf. Process. 32(2) (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Jiang, B., Lan, Y. (2019). Research on Semantic Role Labeling Method. In: Liu, X., Cheng, D., Jinfeng, L. (eds) Communications and Networking. ChinaCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-06161-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-06161-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06160-9
Online ISBN: 978-3-030-06161-6
eBook Packages: Computer ScienceComputer Science (R0)