BERT has made Google better at understanding natural language. This makes it of greater importance that website content is written in a natural and understandable way, rather than using keyword stuffing or other manipulative SEO methods.
Around 10% of all search queries are influenced by Google’s current update BERT. What consequences does this have for earnings expenditure? What effect does this have on those who run websites?
BERT is developed by Google as a machine learning method and stands for “Bidirectional Encoder Representations from Transformers”. The algorithm is specifically designed for natural language processing and is based on NLP (Natural Language Processing) and ANN (Artificial Neural Network).
Historical background of BERT
In October 2018, BERT was released, which fundamentally changed the way Google interprets and processes search queries. Unlike previous approaches that determined the meaning of words using their environment, BERT uses bidirectional analysis within a sentence to identify the meaning of words. This allows for improved context understanding and leads to more accurate responses to search queries.
Before BERT was introduced, Google mainly used a so-called “bag of words” approach, which simply counted the number of certain keywords in a document to determine the relevance of a search query. However, this approach was not able to take into account the context of a question, often producing inaccurate or misleading search results.
With the help of BERT, it becomes possible for Google to understand the language more like a human being.
Example:
For example, if you search for “Apple”, you have to pay attention to whether you need information according to the technology company or the type of fruit. Without the ability to understand context, search results cannot be accurate and either report only on one or the other. But Google’s introduction of BERT has further developed its ability to understand the context of a query and deliver more relevant results.
What does BERT actually do?
BERT – Mode of action in everyday business
BERT enables Google to deliver a better and more accurate response to its users’ search queries by being able to understand the context of each question. For this to be possible, website content must recognize user needs and requirements and respond accordingly. It now allows a search engine to process speech the way a human would, and this is evident in long-tail searches. Prepositions are included in the search results, which was not the case before. As a result, the search engine provides better answers to the questions.
BERT is primarily used for enriching Google search results, but it can equally be used for other implementations of linguistic processing, e.g. Chatbots or text classifications, can be used.
However, with BERT, the user can get the answer to his basic question. This upgrade also affects the Featured Snippets.
BERT summarizes relevant information in a featured snippet
BERT has significant influence on the creation of Google Featured Snippets. These are small summaries of answers for search queries that users want to find on the results page to get an answer quickly. Before BERT, these snippets were generated based on simple word matches, but the model has significantly increased the precision of the snippets by considering the context of the question, allowing it to identify more relevant information and summarize it for a featured snippet.
In this way, BERT helps users get the information they want faster and more accurately, and also increases the relevance and credibility of featured snippets.
It is of utmost importance to write content that is suitable to be displayed in featured snippets to increase traffic and attention.
Is BERT and RankBrain the same thing?
Google has developed BERT and RankBrain to optimize human language processing and search query results. However, these are two completely different technologies.
BERT is not the successor of RankBrain, but the new update is intended to serve as a supplement. Both techniques can be used in the algorithm to provide more accurate results to users. Some queries are answered only by RankBrain, others by BERT.
The RankBrain machine learning model, which was introduced in 2015, is part of Google’s search algorithm and is used to interpret the meaning of search queries and deliver the appropriate search results. In doing so, it has revolutionized the way Google understands and processes search queries.
BERT represents a machine learning model based on the Transformer architecture and designed exclusively for natural language interpretation. Since its release in October 2018, it has significantly changed the way Google understands and processes search queries.
However, it is also possible that multiple techniques are used for the search, such as when Google corrects a misspelled word or/and introduces synonyms. Now prepositions can also be included in the search query. Despite their goal of improving Google results, RankBrain and BERT each have different focuses and purposes of use. RankBrain is an essential part of Google’s search algorithm, while BERT is a specialized machine learning model for natural language handling.
What are the consequences of BERT on search engine optimization?
BERT makes it easier for Google to understand longer and more complex queries, and thus it has become of greater importance to optimize long-tail keywords. The result lists are thus presented in an optimized way and the user benefits from a closer approximation to human communication.
However, SEO changes are not expected from the update. It is advised by Google to continue writing content with users in mind. Currently, the update is only available for English, with a launch for the German variant currently still up in the air.
It is of significant importance that web page content is now written in natural language, more relevant and contextual to achieve an improved position in search results after BERT was introduced.
Google is exhorting site owners to continue creating quality content for their users, but the cost of SEO is steadily increasing, making it increasingly difficult for weekend optimizers and students of online marketing academies to appear on the first page.
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