BERT or Bidirectional Encoder Representations from Transformers


When you hear the name Bert you think about the golden yellow Muppet character in Sesame Street. But this algorithm has nothing to do with the character. Google uses interesting names for its updates (Panda, Penguin, Hummingbird, Pigeon, Mobile, RankBrain, Possum, Fred, Macabees, Maverick), names that resonate with animals, characters and things people like because they want to make them accessible. And even if the terms behind BERT are nerdy, they aren’t too technical and complicated as you dive deep into what the algorithm brings. In simple words, BERT is here to help search engines understand language and phrasing similar to a human. Until now all algorithms functioned like robots but because nowadays people search with phrases depicted from real life, Google needs to update its features.




What is BERT Algorithm?


BERT is the latest Google update that includes Natural Language Processing technology into searching. It focuses on translating the plan of search questions more intuitively. It no longer analyses each word from the phrases, user’s search, but it tries to translate and understand the entire expression to give a more exact outcome. But because alteration of words can affect the result of the search, the update won’t be utilized for 100% of searches. At the start, BERT will control 1 of every 10 indexed lists in the US in English.


Google states that BERT is a complex update and it pushes the breaking points of their equipment, so at the moment they cannot fully utilize it.


Here is an example of how BERT functions.


Before this update, when a user typed “do estheticians stand a lot of work” Google associated the word stand with stand-alone. With BERT in place, Google can interpret the question more accurately.





Does BERT affect SEO?


After every update, people wonder if it influences the way they do SEO. BERT comes with the same question. But it’s important to highlight that it doesn’t impact the way they do SEO if they adopted the best practices and principles from the start. If they base their campaigns on solid keyword research and high-quality content creation, they’ll get great ranking results and boosted performance.


Even if BERT changes the results people get for their queries, it doesn’t really influence the website owners’ side. In October, newspapers announced that New York Times search ranking dropped after BERT update. When promoting this piece of news people didn’t check if this claim is true. Later the data revealed that the update coincided with the move to mobile 1st indexing, so the drop can be the result of this action, and not something BERT caused.


BERT isn’t the type of algorithm that penalizes websites, its purpose is to better understand users’ queries and search intent. SEO creators can win with this algorithm if they make sure they produce content that answers their public. The websites that work as resources for high-quality information to win with this update.


BERT does change something, the type of content the user receives when they type a certain query. Let’s say someone searches “how do I pass a driver’s test”. Before, Google probably returned the WikiHow blog on the subject, to offer the user some tips. Now it sends to the official state driver’s requirements.


BERT encourages natural content


The ones who want to make sure that BERT doesn’t impact their traffic and ranking need to create natural content.


You need to write content in a natural flow that sounds human. Don’t forget that Google isn’t the only one that reads it, people also do it, and they influence traffic more than Google. In the past, Google didn’t clearly state how updates influence ranking, so many websites ranked poorly overnight. Others lost a lot of traffic because they didn’t explain how algorithms function. This time BERT makes clear that everyone who promotes natural content will attract more visitors.


BERT also focuses on the context which it’s crucial these days. The technology behind the algorithm allows it to understand the meaning of the entire query and not only the keywords.


People usually enter a long-phrase when they are searching for specific pieces of content. Before this update, Google used artificial intelligence to analyze each of the words. Now it understands the entire phrase and connects words that are relevant to each other.


How does BERT influence search queries?


To get the picture of how BERT influences search inquiries, we need to discuss the different compounds of the calculation.


BERT conducted solo learning to become the main NLP structure that is pre-prepared on unadulterated plain content.


Bi-directional logical models support relevant understanding because the algorithm puts the words in a sentence and no longer checks them individually. Google it’s a machine, and until recently it battled with this condition because it found it difficult sometimes to deliver relevant results.


Google relies on transformer architecture to examine the connections between words in queries. BERT’s structure is created to examine sentences that have words haphazardly covered out.


One of the most outstanding features of the update is that it anticipates what users want to type or say because it relies on printed entailment to deliver results.


BERT can understand the subtleties of human language and this will translate into better results for queries. Nowadays people are using long questions to search for information, and this update addresses their needs.


It’s expected to influence both text and voice search because it’s designed to help scale conversational search. At present, it’s able to cover only English language queries, but in the past, Google intends to extend it over other languages.




Now that you found out what BERT is and how it impacts queries, you know that you need to create natural content that is contextual and has perspective. With every new update it launches, Google is smarter and advertisers need to react by delivering personalized content to meet the latest requirements.


BERT is a superior tool, Google can now use to serve users and to understand what they need, even when they use ambiguous language.