Recently on October 25th, Google finally announced the brand new iteration of its natural language processing (NLP) capabilities. The name of the latest search algorithm is BERT and it is going to completely transform the way that the search queries works. The reason we call BERT as a major update is because this model might be one of the biggest changes to the natural language processing landscape in the last few years. So, in this article, we are going to take a closer look at BERT and see how it impacts your site.
Why do we need to know what Google algorithms are exactly doing?
If you are a blogger or an SEO specialist, you need to closely follow what Google is doing with its algorithms. Because every single change might have an impact on you and rank your website in a higher or lower position. This is important since it would massively influence your website traffic and brand awareness. So you must keep a note on things they are trying to emphasize with each and every update.
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What does BERT stand for?
In fact, BERT is an abbreviation form of Bidirectional Encoder Representations from Transformers. If you have been following the news, you might know that Google is continuously trying to create a user-centric search algorithm. So, they would be able to offer better and more accurate results to the users. Google believes that using this method would lead to see more accuracy in search results up to 10%. It means that Google’s reading comprehension is getting much closer to humans.
What problems does BERT solve?
One of the biggest problems in traditional natural language processing is machine interpretation. It means a search engine might not understand the user’s intention correctly. As a consequence, it will show an inappropriate result. For example, take a look at these two sentences
- He was addressed kindly
- He addressed the envelope
If you have a closer look at the word “Address” in both sentences, you will immediately figure out that this word has two different meanings in each sentence. However, a machine can not differentiate between these two and this is where the problem starts to emerge.
People these days are relying more and more on search engines as a reference to get the answer to their questions. So the main intention of the search engines should be to understand the intention behind the complex search queries.
BERT is deeply bidirectional. That means not only it takes information from the right-hand side of the token, but also observe the left-hand side to understand the context better.
BERT also composes and decomposes the sentence to understand it better. For doing this, Google uses a transformer that consists of a set of encoders that are chained and working together. Therefore, when you write a text in a search bar, what Google actually does at the first stage, is to separate the text into smaller portions. After that, each small section of the text is assigned to one encoder. Finally, the encoders try to process input vectors to generate encoding. As a result, BERT can focus on every single token and this can significantly improve Google’s comprehension ability.
Google does not want to scan a string and return a collection of the best guesses. Indeed, it wants to make search into a conversation between the user and the intelligent search algorithm.
What is the effect of BERT on SEO?
Considering the way BERT analyzes the long queries, we can understand that Google is paying more attention to user intention. Having said that, we know users are getting used to search longtail keywords. Therefore, pages that merely have focused on the short keywords would get lower score and drop. But the question is how can we optimize for BERT? For answering this question, you need to consider that people are using search engines to find their answers faster and in a more convenient way. So in order to answer the users in the best manner, you need to make your site better for them. It means there is no golden rule that can rank your website higher except aligning what content you have more with what people are actually searching for. In other words, you need to have comprehensive conversational content and serve them up to users.