"Google BERT: Revolutionizing Language Understanding for Smarter Searches"

Google Bert

 Google BERT is a natural language processing model released by Google in 2018.

 Google BERT has significantly improved the accuracy of understanding context and sentence structure, and is used to understand search intent.

This time, we will explain in detail the overview of BERT introduced in the Google search engine, its excellent points, and the ways it is applied in search engines. This is an area that indirectly affects SEO, so those involved in SEO should keep this in mind.

What is BERT?

BERT is a natural language processing model released by Google in 2018. The name BERT is an acronym for "Bidirectional Encoder Representations from Transformers."

In 2019, it was adopted in the Google search engine algorithm and became a hot topic. A feature of Google BERT is its excellent ability to understand context. Its introduction into search engine algorithms has increased the accuracy of understanding search intent from search queries.

BERT is one of the essential technologies for understanding search engine technology.

Let's explain about natural language processing and Transformer, which you should know when deepening your understanding of BERT.

What is Natural Language Processing (NLP)?

BERT is a type of natural language processing. Natural language processing refers to technology that processes the words that people use in their daily lives, including not only written texts but also spoken words.

It is sometimes abbreviated as NLP, an acronym for Natural Language Processing.

Natural language processing first extracts meaning by breaking up sentences and classifying them into nouns, particles, etc. It then analyzes the relationships between words, understands the meaning and context of the text, and converts it into machine language.

Natural language processing has made it possible to understand the context of ambiguous words, such as those used in spoken language. Currently, it is used for text translation functions, speech recognition, predictive conversion, etc.

What is Transformer?

Transformer is essential when understanding natural language processing and BERT. BERT is based on Transformer.

Transformer is a deep learning model announced by Google in 2017. Compared to conventional deep learning, Transformer significantly reduces training time and improves the accuracy of natural language processing.

BERT is the natural language processing technology developed by Transformer. To understand how BERT works, it is a good idea to have some knowledge about Transformer.

Points where BERT is superior

What are the characteristics of BERT, which Google has introduced into its search engine?

The advantages of BERT include the following features:

• Can understand the context
• Good at understanding sentence structure
• Can predict categories and words

Let's explain each in detail.

Can understand the context

Until now, it has been difficult for computers to accurately understand languages. However, with BERT, it is now possible to understand the meaning from the context.

For example, below is the difference in search results before and after the introduction of BERT when searching for "do estheticians stand a lot at work".

Before BERT, we associated the word "stand" in a search term with the term "stand-alone" and did not understand the context correctly.

However, with the introduction of BERT, the level of understanding of context has increased, making it possible to display articles related to standing work.

A major feature of BERT is that it has become possible to read the context between words and the surrounding words.

Good at understanding sentence structure

In addition to understanding context, BERT is also good at understanding sentence structure. Sentences are made up of elements such as particles and nouns.

If you don't understand which words relate to which nouns and the structure of the sentence, you won't be able to read the meaning correctly.

For instance, below is a query for "2019 brazil explorer to USA need a visa."

Before implementing BERT, it was not possible to understand which word "to" in a search query relates to. That's why articles related to travelers traveling to Brazil from the United States are ranked high.

You can see that with the introduction of BERT, articles that match the search intent are now displayed higher.

In order to understand the meaning of a sentence, you need the skills to correctly process the structure of the sentence.

Can predict categories and words

BERT is excellent at understanding the context of a sentence, so it can predict the necessary words from the context of the sentence.

For example, if you enter "SEO" in the search box, the following search queries will appear.

Based on pre-trained logs, it can predict and present terms related to search queries.

With a deeper understanding of language, we can predict which genre a word should be classified into. BERT is also used to predict and classify search queries.

Additionally, it is said that an average of 15% of the queries searched daily are new sentences. BERT is also excellent at prediction, so it can respond to sentences that are being searched for the first time.

How BERT is applied to Google search

We will explain how BERT, which is excellent at understanding context and sentence structure, is applied to Google search.

BERT was introduced into the Google search algorithm in 2019. A major change is that BERT has made it possible to understand the intent of a user's search based on keywords. This leads to improved search accuracy.

You may see featured snippets that appear higher when you search . BERT can now suggest featured snippets that match search intent.

For example, below is an example of a featured snippet when searching for "parking on a hill with no curb."

Before the introduction of BERT, I did not understand that "no" was related to "curb", and I was presented with instructions on how to park on a slope, regardless of whether there was a curb or not.

You can see that with the introduction of BERT, it is now possible to present content based on an accurate understanding of search queries.

In addition, it is thought to be useful in improving the accuracy of suggestions such as ``Others also asked this question'' and ``related keywords'' suggested in SERPs .

Difference with Rankbrain

Even before BERT was introduced into search engine algorithms, Rankbrain supported the quality of search engines.

Rankbrain is the first deep learning module introduced into the Google search algorithm. It was introduced into the Google search algorithm in 2015. Rankbrain helps improve search accuracy by understanding the relevance of search queries and content.

Introduced to the algorithm in 2019, BERT complements Rankbrain and allows us to read more into the context of users' searches than before. It can be said that the understanding of search intent has deepened as Rankbrain and BERT work together.

 SEO measures with BERT in mind?

Is it necessary to take SEO measures into account by considering BERT, which is introduced into the search engine algorithm?

BERT is a technology introduced solely to understand search intent and improve search accuracy.

There is no need to implement SEO measures for BERT. If you are considering BERT as an SEO measure, it is important to create content that meets the search intent and articles that are easy to understand so that the meaning can be understood correctly.

With the introduction of BERT, it is now possible to understand search intent more accurately than before, so providing content that meets the user's search intent will lead to SEO measures.

First of all, be sure to create content that is easy for users to understand, so that the text is not too complex or the content is difficult to understand from the headlines.

It is important to create content that puts the user first.

Example of using BERT

In addition to search engines, BERT is also used in the following services:

• Voice search
• Chatbot
• Summary/translation of sentences

I will give you an example of how to use them one by one.

Voice search

BERT is also used in the voice search field. This is because the accuracy of voice searches has also improved by being able to understand ambiguous words such as spoken language.

With the spread of smart speakers equipped with cloud-based artificial intelligence, we can predict that BERT will be used even more.

chatbot

A chatbot is a program that automatically answers questions and inquiries from users on a website. BERT is also useful for chatbots because it can understand context.

Chatbots can be roughly divided into scenario type and AI type. In the case of a scenario type, the content of the inquiry and question options are prepared in advance, and answers are presented according to the user's selections.

On the other hand, the AI ​​type uses machine learning to provide answers appropriate to the user's questions. It can be said that BERT has made it possible to correctly understand and answer users' questions.

Summary/translation of sentences

Because it can understand context and structure, it is also used in text summaries and language translation tools. When summarizing or translating, it is important to understand dependencies correctly, such as the subject and predicate of a sentence.

It can be said that the introduction of BERT has made it possible to understand the sentence structure, improving the accuracy of summarization and translation.

BERT outlook

With the release of BERT, a natural language processing technology, the accuracy of search engines, chatbots, translations, etc. has improved.

Additionally, with the rise of ChatGPT, the evolution of AI technology is attracting attention.

Let's keep an eye on future trends, such as the evolution of natural language processing technology such as BERT and the introduction of new algorithms.