Semantic hashing

  • Authors:
  • Ruslan Salakhutdinov;Geoffrey Hinton

  • Affiliations:
  • Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario, Canada M5S 3G4;Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario, Canada M5S 3G4

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2009

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Abstract

We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than Latent Semantic Analysis. When the deepest layer is forced to use a small number of binary variables (e.g. 32), the graphical model performs ''semantic hashing'': Documents are mapped to memory addresses in such a way that semantically similar documents are located at nearby addresses. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method. By using semantic hashing to filter the documents given to TF-IDF, we achieve higher accuracy than applying TF-IDF to the entire document set.