An Entity Discover and Linking Approach Based on Convolutional Neural Network and Random Walk with Restart

doi:10.13190/j.jbupt.2017-127

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (1): 65-69.doi: 10.13190/j.jbupt.2017-127

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An Entity Discover and Linking Approach Based on Convolutional Neural Network and Random Walk with Restart

TAN Yong-mei¹, LI Xiao-guang¹, LÜ Xue-qiang²

1. Intelligence Science and Technology Center, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China

Received:2017-07-03 Online:2018-02-28 Published:2018-01-04

Abstract

Abstract: An entity linking approach based on convolutional neural network and random walk with restart was presented. This method first discovers the mentions in the text, after generates the mention candidate entity set, then selects the candidate entity using the entity linking approach based on convolutional neural network and random walk with restart and clusters the mentions those do not have the corresponding entity in the knowledge base. Our method F_CEAFm is 0.652 on the TAC-KBP2016 entity discovery and linking evaluation data set, and the first team is 0.643. The results show our method can effectively solve this problem.

Key words: entity linking, convolutional neural network, random walk with restart

CLC Number:

TN911.22

TAN Yong-mei, LI Xiao-guang, LÜ Xue-qiang. An Entity Discover and Linking Approach Based on Convolutional Neural Network and Random Walk with Restart[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(1): 65-69.

References

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An Entity Discover and Linking Approach Based on Convolutional Neural Network and Random Walk with Restart

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