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Neural KB Embeddings.pdf

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Preview of Neural KB Embeddings
🔗 Source: ixa.ehu.eus
📊 Size: 360 KB
👤 Author: Josu Goikoetxea ; Aitor Soroa ; Eneko Agirre
⬇️ Downloads: 94

Summary

Random walks over knowledge bases like WordNet have been used for word similarity and relatedness tasks, but are slow and have large memory footprints. A novel algorithm combines random walks and neural network language models to produce novel word representations, yielding equal or better results than random walk algorithms with a dense representation (300 dimensions instead of 117K). The word representations are complementary to those of random walks and corpus-based continuous representations, improving state-of-the-art results in similarity datasets.

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Description

Random walks over knowledge bases like WordNet have been used for word similarity and relatedness tasks, but are slow and have large memory footprints.

Technical Information

  • File Format: PDF
  • File Size: 360 KB
  • Pages: 6
  • Language: EN
  • Author: Josu Goikoetxea ; Aitor Soroa ; Eneko Agirre
  • Total Downloads: 94
  • Last Updated: 4 hours ago

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This PDF document about Neural KB Embeddings provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Neural KB Embeddings.

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