Semantic hashing

• Autores: Ruslan Salakhutdinov, Geoffrey E. Hinton
• Localización: International journal of approximate reasoning, ISSN 0888-613X, Vol. 50, Nº 7, 2009, págs. 969-978
• Idioma: inglés
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• Resumen

  • 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