Using Ngram Phrase Models to Generate Site Quality Scores

Photographer: McGeddon
Creative Commons License: Attribution 2.0 Generic

Navneet Panda, whom the Google Panda update is named after, has co-invented a new patent that focuses on site quality scores. It’s worth studying to understand how it determines the quality of sites.

Back in 2013, I wrote the post Google Scoring Gibberish Content to Demote Pages in Rankings, about Google using ngrams from sites and building language models from them to determine if those sites were filled with gibberish, or spammy content. I was reminded of that post when I read this patent.

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Google’s Project Jacquard: Textile-Based Device Controls

Textile Devices with Controls Built into them

I remember my father building some innovative plastics blow molding machines where he added a central processing control device to the machines so that all adjustable controls could be changed from one place. He would have loved seeing what is going on at Google these days, and the hardware that they are working on developing, which focuses on building controls into textiles and plastics.

Outside of search efforts from Google, but it is interesting seeing what else they may get involved in since that is beginning to cover a wider and wider range of things, from self-driving cars to glucose analyzing contact lenses. I was surprised to see a web page from Levi’s showing a joint project from Google and Levis on their Project Jacquard.

This morning I tweeted an article I saw in the Sun, from the UK that was kind of interesting: Seating Plan Google’s creating touch-sensitive car seats that will switch on air con, sat-nav and change music with a BUM WIGGLE

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Citations behind the Google Brain Word Vector Approach


In October of 2015, a new algorithm was announced by members of the Google Brain team, described in this post from Search Engine Land – Meet RankBrain: The Artificial Intelligence That’s Now Processing Google Search Results One of the Google Brain team members who gave Bloomberg News a long interview on Rankbrain, Gregory S. Corrado was a co-inventor on a patent that was granted this August along with other members of the Google Brain team.

In the SEM Post article, RankBrain: Everything We Know About Google’s AI Algorithm we are told that Rankbrain uses concepts from Geoffrey Hinton, involving Thought Vectors. The summary in the description from the patent tells us about how a word vector approach might be used in such a system:

Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. Unknown words in sequences of words can be effectively predicted if the surrounding words are known. Words surrounding a known word in a sequence of words can be effectively predicted. Numerical representations of words in a vocabulary of words can be easily and effectively generated. The numerical representations can reveal semantic and syntactic similarities and relationships between the words that they represent.

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