Google’s Hummingbird Algorithm Ten Years Ago

Added 2013-11-10 – Google was granted a continuation version of this same patent (Search queries improved based on query semantic information) on November 5th, 2013, where the claims section has been completely re-written in some interesting ways. It describes using a substitute term for one of the original terms in the query, and using an inverse document frequency count to see how many times that substitute term appears in the result set for the modified version of the query and for the original version of the query. The timing of this update of the patent is interesting. The link below points to the old version of the patent, so if you want you can compare the claims sections.

Back in September, Google announced that they had started using an algorithm that rewrites queries submitted by searchers which they had given the code name “Hummingbird.” At the time, I was writing a blog post about a patent from Google that seemed like it might be very related to the update because the focus was upon re-writing long and complex queries, while paying more attention to all the words within those queries. I called the post, The Google Hummingbird Patent because the patent seemed to be such a good match.

Hummingbird Image from the Department of Forestry

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Google Scoring Gibberish Content to Demote Pages in Rankings?

This week, Google was awarded a patent that describes how they might score content on how much Gibberish it might contain, which could then be used to demote pages in search results. That gibberish content refers to content that might be representative of spam content.

The patent defines gibberish content on web pages as pages that might contain a number of high value keywords, but might have been generated through:

  • Using low-cost untrained labor (from places like Mechanical Turk)
  • Scraping content and modifying and splicing it randomly
  • Translating from a different language

Gibberish content also tends to include text sequences that are unlikely to represent natural language text strings that often appear in conversational syntax, or that might not be in text strings that might not be structured in conversational syntax, typically occur in resources such as web documents.

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