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Graph-ranking collective Chinese entity linking algorithm

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Abstract

Entity linking (EL) systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base. Existing EL approaches usually ignore the semantic correlation between the mentions in the text, and are limited to the scale of the local knowledge base. In this paper, we propose a novel graphranking collective Chinese entity linking (GRCCEL) algorithm, which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources. By improved weighted word2vec textual similarity and improved PageRank algorithm, more semantic information and structural information can be captured in the document. With an incremental evidence mining process, more powerful discrimination capability for similar entities can be obtained. We evaluate the performance of our algorithm on some open domain corpus. Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

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Acknowledgments

This work was supported in part by the National Basic Research (973) Program of China (2013CB329606) and the Natural Science Research Program of Anhui Science and Technology University (ZRC2016494).

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Correspondence to Tao Xie.

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Tao Xie received the MS degree at the Beijing University of Posts and Telecommunications, China in 2015. His research interests include machine learning and data mining, natural language processing, and social network analysis.

Bin Wu received his PhD degree from the Institute of Computing Technology, Chinese Academy of Science, China in 2002. He is a senior member of CCF. He is currently a professor at the School of Computer Science, Beijing University of Posts and Telecommunication, China. His research interests are in data mining, complex network, and cloud computing. He has published more than 100 papers in referred journals and conferences.

Bingjing Jia is a PhD candidate in the School of Computer Science, Beijing University of Posts and Telecommunications, China. She obtained her Master Degree in 2007. Her research interests cover social network analysis, data mining, and entity disambiguation.

Bai Wang received her PhD degree from Beijing University of Posts and Telecommunications (BUPT), China in 1995. She is currently a professor at the School of Computer Science, BUPT, China. Her research interests are in telecommunication system software, cloud computing, and complex network. She has published 3 books and more than 80 papers.

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Xie, T., Wu, B., Jia, B. et al. Graph-ranking collective Chinese entity linking algorithm. Front. Comput. Sci. 14, 291–303 (2020). https://doi.org/10.1007/s11704-018-7175-0

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