Did you hear about Google’s latest and most important BERT update?

Chances are yes, of-course, BERT is buzzing around the whole internet, so how can you be not aware of it. But why everybody giving so important to this new update. Is it worth learning more about this new update based on NLP (natural language processing)?

Yes, as 1 in 10 search queries will be affected by BERT according to Google.

What is BERT and how will it impact the search?

BERT is the acronym for Bidirectional Encoder Representations from Transformers. BERT is the latest NLP pre-training technique to train AI-based models to understand the contextual meaning of words in a given text. It is the first profound bidirectional, pre-trained contextual based representation of words in a text. It helps Google understand the natural conversational queries from the Web.

It was open-sourced by Google on November 2, 2018.

BERT has enabled Google to better understand the context of search queries to match them with the most helpful results.

Let’s see an example

Credit: Google

Notice the difference in the previous and after results. Before BERT the search results included a book from the “Young Adult” category also. But with the implementation of BERT, the search results are more specifically queuing the best options available across the internet to best match the query “math practice books for adults”.

BERT has certainly improved the Search Query Understanding by being more rational toward the user’s query context.

So, how would you optimize your content to cope with this update?

The obvious answer is by creating Quality Content.

Focus on creating quality content with relevance and usability meant for humans. It does not matter if you are optimizing for short key phrases or long-tail keywords, you need to be focused on the topic and provide the best answer to match the user intent.

Let’s see, for example, the search results for the same query benefits of drawing but with a different conversational approach.

There are 3 different results of query whose context was changed every time we added some more words to it. When analyzing the results, the result pages definitely were very much satisfying the query intent accordingly.

Optimize content for featured snippets by identifying the best opportunities for your keywords in questions, lists, images or text. To earn a featured snippet position, produce quality content and structure it as such to be loved by Google.

Conclusion:

Understanding search intent is a difficult task for search engines and BERT is just the beginning of making search experience naturally better for Google users. However, BERT in itself is not perfect and still needs more perfection to understand language transformations.

A good sign for all those creating and working to provide informational and best quality content for their users.

What do you think about the BERT update?