Disambiguating named entities with deep supervised learning via crowd labels

  • Le-kui Zhou
  • Si-liang Tang
  • Jun Xiao
  • Fei Wu
  • Yue-ting Zhuang
Article

Abstract

Named entity disambiguation (NED) is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom (via human-labeled crowd labels) and deep learning (via human-generated data) for the NED task. In particular, we devise a crowd model to elicit the underlying features (crowd features) from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network (DCNN). The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge (via crowd labels) into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.

Key words

Named entity disambiguation Crowdsourcing Deep learning 

CLC number

TP391.4 

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Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Le-kui Zhou
    • 1
  • Si-liang Tang
    • 1
  • Jun Xiao
    • 1
  • Fei Wu
    • 1
  • Yue-ting Zhuang
    • 1
  1. 1.Institute of Artificial Intelligence, College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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