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Constructing Semantic Hierarchies via Fusion Learning Architecture

  • Tianwen Jiang
  • Ming Liu
  • Bing QinEmail author
  • Ting Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)

Abstract

Semantic hierarchies construction means to build structure of concepts linked by hypernym-hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of hypernym-hyponym (“is-a”) relations. We propose a fusion learning architecture based on word embeddings for constructing semantic hierarchies, composed of discriminative generative fusion architecture and a very simple lexical structure rule for assisting, getting an F1-score of 74.20% with 91.60% precision-value, outperforming the state-of-the-art methods on a manually labeled test dataset. Subsequently, combining our method with manually-built hierarchies can further improve F1-score to 82.01%. Besides, the fusion learning architecture is language-independent.

Keywords

Semantic hierarchies Hypernym-hyponym relation Fusion learning architecture 

Notes

Fundings

The research in this paper is supported by National Natural Science Foundation of China (No. 61632011, No. 61772156), National High-tech R&D Program (863 Program) (No. 2015AA015407).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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