Searching for information is an integral part of our lives and, on the Internet, the ability of search engines to understand our intentions and present relevant results is crucial.
In this context, a disruptive innovation has made its mark in the field of natural language processing: BERT. Since its introduction, it has transformed the way search engines interpret and respond to user queries.
However, you may not know everything about it, how it works, its features or how it impacted SEO in recent years. That’s why today we want to tell you about it, so you can see the before and after of BERT.
Table of Content
What is BERT?
BERT is an acronym for Bidirectional Encoder Representations from Transformers, a pre-trained language model developed by Google AI in 2018. It’s part of the Transformers architecture-based family of models, which is a revolutionary approach in the field of natural language processing (NLP).
Because of this, BERT has gained fame in a short time for its ability to understand the context of words in a sentence, both the words preceding and following it.
BERT’s distinguishing feature is its ability to process text bidirectionally, meaning that it considers both the preceding and following context of each word when generating word representations.
This allows BERT to capture deeper relationships and meanings in language, which in turn improves its performance in a variety of natural language processing tasks, such as reading comprehension, sentiment analysis, machine translation, among others.
This new system is trained by using large amounts of unlabeled text, which allows it to learn representations of words and general phrases. These pre-trained representations can then be adjusted for specific tasks with smaller labeled data sets.
This ability to fit pre-trained models to specific tasks is known as transfer learning, and has been shown to be effective in natural language processing.
Since the introduction of BERT, many variants and improvements in the transformer architecture have emerged, leading to rapid progress in the NLP field and driving the development of even more advanced and powerful models.
How has BERT impacted SEO?
BERT has had a significant impact on the field of search engine optimization or SEO, although it’s important to understand that it’s not a direct ranking factor.
Instead, BERT affects the way search engines understand and process web page content, which can indirectly influence SEO performance.
1. Improvement in the understanding of context and intent
It helps search engines to better understand the context and intent behind search queries, i.e., search results can be more relevant to users’ questions, which in turn improves the search experience and can increase the visibility of relevant content in the results.
2. Semantic search
Improves the ability of search engines to understand semantic relationships between words and phrases. This allows search engines to identify content that not only contains exact keywords, but also relates to the overall topic of the query, which can benefit websites that offer valuable and consistent content.
3. Quality content
Because BERT focuses on contextual understanding, websites that provide high-quality, relevant content can be rewarded with increased visibility in search results. The emphasis is on creating useful and valuable content for users, rather than simply focusing on keyword optimization.
4. Conversations and natural language
The ability of search engines to interpret natural language and conversational queries has also improved. This is important and relevant for voice searches, as people tend to use more natural and longer language when speaking compared to more concise written searches.
5. Changes in traffic and ranking
While BERT is not a direct ranking factor, the algorithm updates they incorporate can influence search rankings. Some websites may experience changes in traffic due to the way BERT changes the interpretation of certain queries and the relevance of results.
How does the new BERT algorithm work?
It works by means of a transformer-based architecture, which is a neural language model, and uses a pre-training and fine-tuning approach to capture and apply natural language knowledge in various language processing tasks. However, this whole process can be segmented:
BERT is trained on large amounts of unlabeled text. During this phase, the model predicts the next word in a sentence based on both preceding and following context. This allows it to capture deeper semantic and contextual relationships in language.
In addition, BERT is also trained to solve tasks such as masked word prediction, where some words are hidden and the model must guess them; and sentence pair classification. These tasks help the model learn general language representations.
2. Fine tuning
After the pre-training phase, BERT is adjusted or fine-tuned for specific tasks by using smaller labeled data sets. At this stage, the model is trained to understand and perform specific tasks, such as sentiment analysis, text classification, or question answering.
3. Transformer architecture
It uses a transformer architecture, which consists of attention and transformation layers. Attention allows the model to take into consideration all words in a sentence by generating representations for each word, rather than relying solely on the preceding context.
Breaks text into smaller units called tokens. These can be individual words or parts of words. In addition, BERT adds special tokens to indicate the beginning and end of sentences, as well as to mask words during pre-training.
5. Layers and dimensions
BERT has multiple layers and dimensions in its architecture. The more layers and dimensions it has, the deeper and more complex the model is, which can improve its ability to capture complex relationships in the language.
How to improve SEO for Google BERT?
Improving SEO in the context of Google BERT involves focusing on creating high-quality, relevant content that aligns with how this new system understands natural language and users’ search intentions. Here are some strategies to improve your SEO in the BERT era:
1. Quality content
Create valuable, high-quality content that is relevant to your audience. BERT focuses on understanding the context and intent behind queries, so it’s important that your content fully and accurately answers users’ questions and needs.
2. Natural and conversational language
Make sure you use natural and conversational language in your content. BERT takes natural speech and conversations into account, so writing fluently and consistently can help make your content more easily understandable to the algorithm and to users.
3. Keyword research
Conduct thorough keyword research to understand the queries your target audience uses in search engines. Focus on long-tail keywords and phrases that reflect the most specific search intentions.
4. Detailed and complete content
Provide detailed and complete information in your content. BERT seeks to understand context, so addressing a topic comprehensively can increase your visibility in search results.
5. Answering frequently asked questions
Consider including FAQ sections on your pages. Direct questions and answers can help BERT identify the relevance and context of your content in relation to user queries.
6. Structure and formatting
Use a clear structure and readable format for your content, as this can improve the user experience and make it easier for BERT to understand the organization and flow of your content.
7. Updating content
Keep your content current and relevant because frequent updates can help keep your content aligned with trends and changing user needs.
8. Testing and optimization
Test and analyze the performance of your content in search results. See how BERT interprets your pages and adjust your approach based on the results.
9. User feedback
Pay attention to user feedback and their interaction with your content, as it can provide you with valuable information about how BERT is interpreting and presenting your pages in search results.
10. Technical optimization
Make sure your website is strong on the technical side as content alone is not enough. Make sure you have fast load times, a consistent internal link structure and proper indexing by search engines.
Before and after BERT
If there’s one thing that’s clear, it’s that this new algorithm is here to stay at Google, as it’s shaping up to be the new revolution in the way quality content is offered to internet users, so let’s see what it was like before and what it could be like from now on:
- Emphasis on exact keywords: the main focus was on optimizing exact keywords to improve search engine rankings.
- Difficulty in contextual understanding: search engines had difficulty understanding the full context and intent behind search queries.
- Less relevant results: sometimes, search results were not as relevant due to lack of contextual understanding.
- Focus on short and concise content: content often focused on being short and concise, sometimes sacrificing detail and depth.
- More rigid language: the language used was often more rigid and did not reflect natural, conversational speech.
- Performance on conversational searches: search engines struggled with understanding conversational searches and complex queries.
2. After BERT
- Improved contextual understanding: BERT and similar models enable better understanding of the context and intent behind queries.
- Focus on search intent: Search engines place more importance on understanding the intent behind queries and providing relevant results.
- More accurate results: search results tend to be more accurate and relevant due to improved contextual understanding.
- Detailed and extensive content: detailed and comprehensive content that addresses users’ needs in depth is more valued.
- Natural and conversational language: the use of natural and conversational language is more important to match the way people search.
- Improved conversational searches: BERT enables better interpretation of conversational searches and long queries.
However, there’s one detail to keep in mind, yes, BERT is the new algorithm that is here to stay, but it’s far from perfect.
There’s still a lot of room for improvement and updates to the algorithm continue, so keep an eye on the sources where these changes are shared, but do you know what they are? Leave us your sources in the comments.