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Constructing Document Vectors Using Kernel Density Estimates

  • Michael Mayo
  • Sean Goltz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10571)

Abstract

Document vector embeddings are numeric fixed length representations of text documents that can be used for machine learning and text mining purposes. We describe in this paper a new technique for generating document vectors. Our novel idea builds on the recently popular notion of neural word vector embeddings and combines this concept with the statistics of kernel density estimation. We show that robust document vectors can be produced using our new algorithm, and perform an experiment involving several challenging text classification datasets to demonstrate its effectiveness.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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