In October 2019, Google unveiled a new algorithm again! An algorithm called BERT, which is one of Google’s new algorithms. BERT can be considered somewhat similar to Rank Brin.

If we want to give a brief explanation about Rank Brin, we can say that Rank Brin considers the search of the audience and shows the final result to the user according to the place of residence, previous search records, etc. But the BERT algorithm, with the help of artificial intelligence, helps to make this more accurate. We can say that the BERT algorithm came to complement the Rank Brin algorithm.

In the following article, you will know how artificial intelligence is prepared with the help of search engines such as Google so that they can reach the final goal of users and have a tremendous impact on SEO by optimizing search results.

What is the Bert algorithm and what is its purpose?

If we want to define Bert algorithm in simple language, this algorithm is a model of colloquial language that helps the rest of Google algorithms to understand even the smallest parts of colloquial language such as prepositions like a human.

The definition of Bert algorithm is familiar and colloquial:

This algorithm has come to understand the meaning of the sentences correctly and bring the user to the final goal!

Scientific definition of Bert algorithm

The Bert algorithm is a natural language processing (NLP) model that helps Google better understand human language and generally display results closer to the user’s goal.

Google has been working on an open source project in recent years. Its name was BERT. Exactly the same name he gave to his new algorithm.

Bert stands for “Bidirectional Encoder Representations from Transformer”. Knowing this meaning and concept has little to do with our field of work and goes back more to artificial intelligence. So it is better that you focus more on how it works. Google has always tried to understand the meaning of the terms searched by users, but it has been very difficult for it.

The conversation between two people is very complicated. Because everyone uses prepositions and verbs in the same way, all people also speak a certain language and dialect. When people talk to each other, they simply understand these concepts. But when it comes to the relationship between humans and algorithms and machines, it is very difficult for them to discern the general meaning of the sentence.

That’s why until a few years ago, if you did a Google search for a sentence, Google would try to turn it into separate words in response. For this reason, each word in the sentence would show you a result or show similar results to the meaning of some of our sentence words, but this is not the case now. This made humans act like a robot to search, so that they could adapt to Google’s algorithms.

But over time, Google is trying to fix this big weakness, for example, by adding voice search. Google introduced the Bert algorithm to complement its former algorithms. Bert is based on a neural network for natural language processing called NLP.

General summary of Bert:

Algorithms can understand our language in exactly the same way we use it.
Google search algorithms can analyze users’ queries more accurately by better understanding our language. So by analyzing queries, it can display the purpose of users correctly.

Natural Language Processing (NLP)

Natural language processing is one of the branches of artificial intelligence that deals with interactions between computers and humans through natural language. The ultimate goal of NLP is to read, decode, understand and comprehend human language in a valuable way. Most natural language processing methods for extracting and understanding the meaning of human language are based on machine learning techniques.

Natural language processing is used in Google Translate, voice assistants such as Siri, text editors such as Word, and more.

Computers can use natural processing language to speak to humans in their own language, understand their words, analyze them, and identify important parts of it. Today’s machines can analyze larger volumes of textual data in less time than humans. In addition, computers are free from errors and prejudices. The idea of ​​the large amount of textual data that is generated every day, especially on social networks, forces us to use natural language processing.

Natural language challenge

Understanding how words fit into structure and meaning is a field of linguistic study. Understanding Natural Language (NLU), or NLP, as it is otherwise known, dates back more than 60 years to the original Turing Test article and definitions of what constitutes AI, and possibly earlier.

This fascinating field faces unresolved problems. Many of them are related to the ambiguous nature of language (lexical ambiguity). Almost every other word in English has several meanings. These challenges naturally extend to a growing network of content; Because search engines try to interpret the goal to meet the information needs expressed by users in written and spoken questions.

Vocabulary ambiguity

In linguistics, ambiguity is beyond the level of the word in the sentence. Words with multiple meanings become more difficult to understand ambiguous sentences and phrases together. “The biggest bottleneck for computational knowledge is the killer of all natural language processing.” Also, words that have the same spelling and different meanings or are spelled differently in English examples but are the same sound.

How do search engines learn a language?

How can search engines understand these languages ​​and show the user’s purpose? In computational linguistics, concurrence holds true the idea that words with similar meanings or related words tend to live very close together in natural language. In other words, they tend to be adjacent (sometimes referred to) in sentences and paragraphs or text.

This field of studying the relations and synchronicity of words is called Firth linguistics, and its roots are usually linked to the linguist John Firth of the 1950s. In Firth linguistics, words and concepts that live together in spaces adjacent to the text are similar or related. Like the word bus and car are in the family of vehicles.

Despite all the advances in search engines and computational linguists, unsupervised and semi-regulatory approaches such as Word2Vec and Google Pygmalion have some shortcomings that prevent them from understanding the scale of the human language.

Site optimization for Bert algorithm

Bert is an artificial intelligence algorithm that is constantly learning. All you can do is really write for the end user. To be successful in optimization, you need to change your perspective on keywords and phrases. The playground has changed and the use of old techniques is useless.

The previous method of SEO work to optimize the site was as follows:

  1. Find keywords
  2. Repeating it in titles and text

But now you have to pay attention to the concept that the user is looking for. The more carefully you search for user searches, the more successful you will be. Fortunately, this reduces the use of keywords or keyword stuffing.

One of the things that Bert algorithm does well is predict the target of a user search with a low error rate. This means that if you type one of the phrase words incorrectly but the other words have the same meaning, Google will understand what you mean and you will be surprised to see that in the results it shows you what was on your mind!

So we can conclude that:

The main purpose of the BERT algorithm is to better and more accurately understand the purpose of the user search.

What makes Bert algorithm special?

There are several elements that make Bert very special to search for and beyond (the world – yes, is so large that it is a research base for natural language processing). Several special features can be found:

  • Two-way
  • Encoder
  • The show
  • Converters

Bert is the first model of a completely two-way natural language, but what does that mean? The part of speech to which a particular word belongs can change with the development of the sentence in the true sense of the word. The real understanding of a text is that you can see all the words in a sentence at the same time and understand how all the words affect the text of the other words in the sentence. Bert is able to look at both sides of a word and the whole sentence at the same time so that one looks at the whole text of a sentence rather than just a part of it. The whole sentence, both left and right of a target word can be considered simultaneously in the text.

Differences and similarities between Bert and Rank Brin algorithms

The goal of both algorithms is to better understand users’ search so that they can show the best and closest results to them. Both algorithms are controlled by Hummingbird and use artificial intelligence.

But the difference:

Bert algorithm seeks to understand natural language more, but Rank Brin algorithm seeks to find long and unknown concepts. Note that Bert is not a substitute for Rank Brin. In fact, Bert algorithm is an add-on to Google ranking system and an additional way to understand search.

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