‘Using word vectors and applying them in SEO’ – Search Engine Land
JR Oakes says, “Today, the SEO world is abuzz with the term “relevancy.” Google has gone well past keywords and their frequency to looking at the meaning imparted by the words and how they relate to the query at hand.
In fact, for years, the common term used for working with text and language had been natural language processing (NLP). The new focus, though, is natural language understanding (NLU). In the following paragraphs, we want to introduce you to a machine-learning product that has been very helpful in quantifying and enhancing relevancy of content.
Earlier this year, we started training models based on a code base called Char-rnn from Andrej Karpathy. The really interesting thing about this code base was that you could (after training) end up with a model that would generate content based on what it learned from the training documents. It wouldn’t just repeat the content, but it would generate new readable (although quite nonsensical) content.
It operates by using a neural network to learn which character to guess next. If you have the time, Karpathy’s write-up is a fascinating read that will help you understand a bit more about how this works”.
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