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Named Entity Disambiguation via Probabilistic Graphical Model with Embedding Features

  • Weixin Zeng
  • Jiuyang Tang
  • Xiang Zhao
  • Bin Ge
  • Weidong Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)

Abstract

Named entity disambiguation (NED) is the task of linking ambiguous mentions in text to their corresponding entities in a given knowledge base, such as Wikipedia. State-of-the-art NED solutions harness neural networks to generate abstract representations, i.e., embeddings, of mentions and entities, based on which the disambiguation process can be achieved by finding entity with the most similar representation to mention. Nevertheless, the coherence among mentions, and their corresponding entities, is yet neglected. To fill this gap, in this work, we put forward intra, an approach effectively integrating embedding features into a collective disambiguation framework, i.e., probabilistic graphical model. Markov Chain Monte Carlo sampling and SampleRank algorithm are implemented for model parameters learning and inference. We evaluate intra on existing dataset against several state-of-the-art NED systems, which validates the effectiveness of our proposed method.

Keywords

Named entity disambiguation Probabilistic graphical model 

Notes

Acknowledgments

This work was partially supported by NSFC under grants Nos. 61872446, 71690233 and 71331008.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Weixin Zeng
    • 1
  • Jiuyang Tang
    • 1
    • 2
  • Xiang Zhao
    • 1
    • 2
  • Bin Ge
    • 1
    • 2
  • Weidong Xiao
    • 1
    • 2
  1. 1.College of System EngineeringNational University of Defense TechnologyChangshaChina
  2. 2.Collaborative Innovation Center of Geospatial TechnologyWuhanChina

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