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Wikifying Novel Words to Mixtures of Wikipedia Senses by Structured Sparse Coding

  • Balázs Pintér
  • Gyula Vörös
  • Zoltán Szabó
  • András Lőrincz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 318)

Abstract

We extend the scope of Wikification to novel words by relaxing two premises of Wikification: (i) we wikify without using the surface form of the word (ii) to a mixture of Wikipedia senses instead of a single sense. We identify two types of “novel” words: words where the connection between their surface form and their meaning is broken (e.g., a misspelled word), and words where there is no meaning to connect to—the meaning itself is also novel. We propose a method capable of wikifying both types of novel words while also dealing with the inherently large-scale disambiguation problem. We show that the method can disambiguate between up to 1,000 Wikipedia senses, and it can explain words with novel meaning as a mixture of other, possibly related senses. This mixture representation compares favorably to the widely used bag of words representation.

Keywords

Interpreting novel words Wikification Link disambiguation Natural language processing Structured sparse coding 

Notes

Acknowledgments

The research has been supported by the ‘European Robotic Surgery’ EC FP7 grant (no.: 288233). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of other members of the consortium or the European Commission. The research was carried out as part of the EITKIC_12-1-2012-0001 project, which is supported by the Hungarian Government, managed by the National Development Agency, financed by the Research and Technology Innovation Fund and was performed in cooperation with the EIT ICT Labs Budapest Associate Partner Group.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Balázs Pintér
    • 1
  • Gyula Vörös
    • 1
  • Zoltán Szabó
    • 1
    • 2
  • András Lőrincz
    • 1
  1. 1.Faculty of InformaticsEötvös Loránd UniversityBudapestHungary
  2. 2.Gatsby Computational Neuroscience UnitUniversity College LondonLondonUK

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