Heard of Google’s new update, BERT?
You probably have if you’re big on search engine optimization (SEO).
The hype of over Google BERT in the SEO world is justified because BERT is making search more about the semantics or meaning behind the words rather than the words themselves. In other words, search intent is more significant than ever.
Google’s recent update of BERT affecting the SEO world impacts 1 in 10 search queries, and Google projects that this will increase over more languages and locales over time.
Because of the huge impact BERT will have on searches, having quality content is more important than ever.
So that your content can perform its best for BERT (and for search intent), in this article, we’ll go over how BERT works with searches and how you can use BERT to bring more traffic to your site.
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What is BERT?
BERT stands for Bidirectional Encoder Representations from Transformers.
Now that’s a term loaded with some very technical machine learning jargon!
What it means:
- Bidirectional: BERT encodes sentences in both directions simultaneously
- Encoder representations: BERT translates the sentences into representations of word meaning it can understand
- Transformers: Allows BERT to encode every word in the sentence with a relative position since the context in large part depends on word order (which is a more efficient method than remembering exactly how the sentences were entered into the framework)
If you were to reword this, you could say that BERT uses transformers to encode representations of words on either side of a target word.
At its base, the name means that BERT is a brand-new, never-been-accomplished-before, state-of-the-art natural language processing (NLP) algorithm framework. This type of structure adds a layer of machine learning to Google’s AI designed to understand human language better.
In other words, with this new update, Google’s AI algorithms can read sentences and queries with a higher level of human contextual understanding and common sense than ever before.
While it doesn’t understand language at the same level that humans can, it still is a massive step forward for NLP into the linguistic understanding of machines.