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Deep neural network-based relation extraction: an overview

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Abstract

Knowledge is a formal way of understanding the world, providing human-level cognition and intelligence for the next-generation artificial intelligence (AI). An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), plays a vital role in Natural Language Processing (NLP). To date, there are amount of studies for RE in previous works, among which these technologies based on deep neural networks (DNNs) have become the mainstream direction of this research. In particular, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE, whose various evolutions on structure and settings have affected this task. Understanding the model structure and related settings will give the researchers a deep insight into RE. However, little research has been done on them. Hence, this paper starts from these two points and carries out analysis around the mainstream research routes, supervised and distant supervision. Meanwhile, we classify all related works according to the evolution of model structure to facilitate the analysis. Finally, we discuss some challenges of RE and give out our conclusion.

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Notes

  1. This tool in this work can be downloaded from here http://nlp.stanford.edu/software/lex-parser.shtml.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. U19A2059), the Fundamental Research Funds for the Central Universities (NO. ZYGX2020ZB034), and Sichuan Science and Technology Program (NO. 2019YFG0507 & 2020YFG0328). We sincerely thank Mr. Kombou Victor, Anto Leoba Jonathan and Wilson Jim Owusu for their helpful discussions.

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  1. Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hailin Wang, Ke Qin, Rufai Yusuf Zakari, Guoming Lu & Jin Yin

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Wang, H., Qin, K., Zakari, R.Y. et al. Deep neural network-based relation extraction: an overview. Neural Comput & Applic 34, 4781–4801 (2022). https://doi.org/10.1007/s00521-021-06667-3

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