What is the BERT Language Model and How Does It Work?
Every day, billions of people search for various topics using Google. Google, in turn, strives to provide them with the most relevant and related results in a short period to enhance the user experience. To achieve this, Google continuously analyzes user queries to better understand their intent (search intent) and deliver the most appropriate answers. For this purpose, Google employs machine learning technologies, natural language processing (NLP), and regularly improves its search algorithms and artificial intelligence-based systems. An example of these innovations is the BERT (Bidirectional Encoder Representations from Transformers) language model, which enables Google to understand the search context more deeply and better meet users' needs.
What is the BERT language model?
Sometimes, when users search for a specific topic, they may struggle to clearly articulate their thoughts or may not know which phrases to use to obtain the best results. In such cases, they face difficulties in finding the necessary information, which negatively impacts the user experience. The BERT language model is an algorithm designed to better understand user queries and provide them with the most relevant results.
The algorithm works as follows: when a user performs a search, the words in a sentence or phrase are not analyzed individually but rather in conjunction with the surrounding words. Based on the model, the meaning of a word is determined by considering the words before and after it. For example, in the phrase "spring vacation," to determine the meaning of the word "spring," the model also considers the word "vacation" and understands that the query is referring to the season.
Additionally, in the past, Google did not consider function words such as "for," "to," etc., which sometimes prevented it from fully understanding user requests. With the implementation of the BERT language model, this issue has been resolved, allowing Google to accurately interpret queries where function words carry significant meaning. As a result, even complex queries written in a conversational style are now answered with more relevant results. The application of the BERT language model in both search ranking and featured snippets helps users find useful information that aligns with their needs.
Examples Related to the BERT Algorithm
Let’s take a look at the phrase "2019 brazil traveler to USA need a visa." The search is about whether a Brazilian citizen needs a visa when traveling to the USA. Before the implementation of the BERT algorithm, this query was often misinterpreted, leading to results relevant to a U.S. citizen traveling to Brazil instead. However, with BERT, Google was able to correctly understand the main intent of the search. By analyzing the word "to" in relation to the surrounding words, the algorithm recognized that the traveler was a Brazilian citizen heading to the USA, not the other way around. This allowed Google to provide more accurate and relevant search results.
If we look at another example, we can see that even when the user does not explicitly mention the name of the Eiffel Tower, Google is still able to understand what the user is referring to based on the words in the query. Thanks to the BERT algorithm, Google accurately interprets the intent behind the search and provides the correct answer in the featured snippet section of the search results page. This demonstrates how BERT helps Google grasp the context of a query, even when certain keywords are missing, ensuring that users receive the most relevant and useful information.
In the first part of this example, the user searched for “parking on a hill with no curb,” intending to refer to parking on a hill without a curb. However, Google ignored the word “no” and responded to the query in the opposite form. In the second part, the BERT algorithm analyzed all the words together, ensuring that the query was successfully answered.
What Should We Pay Attention to After the BERT Language Model?
There is no specific optimization process tailored to the functionality of the BERT algorithm. However, several processes we implement to align our website with Google's criteria indirectly help us adapt to BERT as well. For example, instead of merely using keywords, writing content in a natural and high-quality manner makes it more readable, which is considered one of the key aspects of optimization. During the optimization process, you should not only focus on search engines but also ensure that users can benefit from your content as much as possible and that their attention is captured.
As a result, the BERT language model deeply understands the essence of the content, providing users with more precise and useful search results. This means that when creating content, you should prioritize the value of the information and the user experience rather than just keywords. Otherwise, your content will be considered worthless to users, and as a result, your website will decline in search rankings.
