Introduction to BERT and its impact on SEO

What is BERT?

BERT, or Bidirectional Encoder Representations from Transformers, is a machine learning model aimed at solving various problems in the field of natural language processing (NLP). Google made this algorithm public in 2018, and it represented a monumental leap in a machine's ability to understand the semantics and context behind a string of words. Unlike previous models that used a one-way approach, BERT learns the relationships between all words in a sentence. This information allows you to more accurately understand the meaning of a word in a specific context. This has an impact on various applications that use text data, including search engine queries.

Before BERT, search algorithms had difficulty understanding the meaning behind a string of words. For example, the word "bank" can mean a financial institution or a river bank, depending on how it is used in a sentence. BERT resolves this ambiguity by understanding context, thereby providing more accurate search results. This deep understanding of language is a major milestone in the development of NLP and was introduced into Google Search to produce more relevant results for user queries.

Why does BERT matter for SEO?

SEO, or search engine optimization, has had to evolve quickly to keep up with Google's ever-changing algorithms. The emergence of BERT was a turning point for SEO specialists. Previously, you could get by by simply typing keywords into web pages. With the advent of BERT, Google's algorithm gained the ability to understand context and semantics, making keyword stuffing an obsolete strategy. The key ranking factor now is creating content that answers the questions users actually ask.

The BERT algorithm is focused on understanding the natural flow of language and context, so SEO practitioners must adapt to these changes. The quality of the content has become a decisive factor: it must be accurate, understandable and fully satisfy the user's request. In the era of BERT, understanding user intent and providing relevant information has become more important than ever. Google rewards sites that offer valuable content, so focusing on quality rather than quantity will help your site rank better in search results.

How does BERT work?

Understanding how BERT works requires diving into its mechanism. It uses Transformer architecture to take into account the full context of a word by looking at the words that come before and after it. This model processes words in relation to all other words in a sentence, unlike directional models that read text from left to right or right to left. This bidirectional understanding is very important for understanding the context of each word in a sentence.

For example, let’s take the query “How to catch fish.”Previous algorithms mostly focused on keywords like "catch" and "fish", perhaps ignoring the word "how". BERT can understand that a user is looking for guidance or steps on how to catch fish and thus produce results that are more in line with the user's intent. This ability to understand context is a game-changer for search algorithms, so SEOs need to consider BERT's impact on search behavior.

Unpacking the Bidirectional Nature of BERT

The bidirectional nature of BERT is a radical departure from previous NLP models. Traditional algorithms scanned text either from left to right or vice versa. This unidirectional approach limited understanding of context because each word was viewed only in relation to the word that preceded or followed. In contrast, BERT's bidirectional approach allows us to understand context from two sides.

The impact of this bidirectional understanding is enormous, especially for queries in which the prepositions “to,” “for,” and “with” play a significant role. For example, in the query “flights to London from New York,” understanding the importance of the words “to” and “with” can dramatically change the search results. In the world of SEO, this means that content must be more closely tailored to the user's intent, making context more important than ever.

Semantic Search and BERT

The impact of BERT goes beyond simply understanding how words relate to each other, it allows you to gain a deeper understanding of the semantic meaning of a search query. Semantic search is the interpretation of the intent or "why" behind a query. BERT's ability to understand the nuances of human language enables search engines to return results that not only contain keywords, but also match the user's intent.

These advanced features have raised the bar for content creators. It’s no longer enough to just use keywords; the content needs to meaningfully answer the user’s query. In terms of semantic search, long-tail keywords, which often reflect the specific intent of the user, are becoming increasingly important. For example, instead of focusing on the keyword “SEO,” you should use long-tail keywords like “how SEO increases website traffic” to match specific queries.

Featured Snippets: More accurate than ever

Featured snippets aren't a new concept: they're framed information that appears at the top of many search results pages. However, BERT has significantly improved the accuracy of these snippets. Because the algorithm understands the query better, it can get more relevant information to display as a snippet.

Before BERT, snippets sometimes did not provide an adequate answer to a query.They often focused on keywords but did not consider context, resulting in irrelevant or incomplete answers. With the increased insight provided by BERT, snippets have become more useful, making them a prime target for SEOs. For your content to rank, it must directly answer the questions users are most likely to ask, and do so in a clear and concise manner.

Local SEO gets a new look

Local search often uses queries formulated in a more conversational or casual manner. The query may contain prepositions such as "near", "near" or "close to", all of which BERT is designed to understand better. As a result, local businesses benefit because the search engine can better match their services or products to local queries.

Before BERT, a search query like “best pediatricians near me” could produce results that focused on the keyword “best pediatricians,” potentially ignoring the important “near me” part. With BERT, the algorithm can understand that a user is looking for high-quality pediatricians in the immediate area and return a much more useful and relevant list. If you're a local business, optimizing for local SEO has never been more important.

Voice Search and Spoken Queries

Voice search is becoming increasingly popular: by 2022, about 50% of all searches are predicted to be voice searches. These search queries tend to be more conversational in nature and may include full sentences or questions. BERT's ability to understand the natural flow of language and its context makes it highly compatible with voice search.

For example, if a user's voice search asks, "What's the best way to cook pasta?", BERT can understand that they're likely looking for a step-by-step guide or recipe. Therefore, SEO strategies must adapt to such conversational queries, focusing on providing direct and complete answers that satisfy the specific intent of the user.

Focus on user intent

In the era of BERT, understanding the user intent behind search queries has become more important than ever. The algorithm strives to match each query with the most relevant content, which means it's not just about the words in the search bar, but also what's behind them. SEO strategies must analyze user intent to provide content that not only ranks well, but also solves a user's problem or answers a user's question.

In order to better target user intent, site content must be carefully crafted.It should not only contain keywords, but also offer deep and valuable information that meets the various purposes of users, be it searching for quick answers, instruction manuals, or in-depth analysis.

Conclusion

The introduction of BERT has revolutionized the field of SEO. From understanding the context of search queries to emphasizing the importance of semantic search, this has raised the stakes for content creators and SEOs alike. Although the algorithm is complex, its essence is simple: create high-quality, relevant content that is focused on fulfilling user intent. This way, you not only increase your chances of ranking higher, but also provide valuable content that meets the needs of your audience.