Google BERT

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Definition

Google BERT or Bidirectional Encoder Representations from Transformers is a learning model created by Google to improve natural language processing on their search engines. It was introduced as a Google algorithm update in 2019 to help better understand the language used by search engine users. AI language models power BERT. The model is applied to search results and helps Google break down the context around our searches. It dissects a search query and processes every word in the context of other words in the query.

Quick Access

Queries are processed bidirectionally through BERT. This means that any sentence can be analysed in both directions, frontwards or backward, allowing for a further improved understanding of the context of a search query. The BERT model helps Google to improve language understanding to optimise their results on SERPs for users and accurately match search intent.

How does the Google BERT model work?

The Google BERT model is designed using AI tools and natural language processing methods. It is used to understand Google search queries in a more human context. Full sentences are considered rather than the focus placed on individual words. There comes a better understanding of the relationships between words, particularly prepositions.

Important features

Transformer neural architecture. BERT is a transformer model that consists of 2 important mechanisms, encoders and decoders. These 2 elements work to understand the links between words in a sentence. The encoder reads what has been input into the search engine. The decoder processes and generates the prediction of the task that is at hand.
Bidirectional reading. BERT reads and analyses sentences in both directions, from left to right and right to left. It can even analyse both directions at the same time. By doing this, the model helps Google to understand the context of the search query better.
Pre-training and finetuning. The natural language processing model is first trained on large text data, e.g. Wikipedia pages, and then it is fine-tuned to complete specific tasks. These tasks include predicting masked words based on context and learning about sentence relationships.

All in all, the model helps the Google algorithm select search results that will best meet a search engine user’s intent. They should be provided with the most relevant SERP results. The model is particularly useful for long-tail queries where Google may struggle to dissect the meaning of a sentence altogether. It can understand the connection between words. However, be careful, as not every search query is always impacted. The more conversational the query is, the harder it will be for BERT to dissect and understand. Google has designed other tools such as Google USE that can further understand complicated queries like these.

Examples

Transformer archictecture

The BERT algorithm uses encoders and decoders to process language naturally and understand the links between words. Let’s take an example of the query “How to open a bank account as a foreigner in Spain?” Before the BERT algorithm was implemented, Google may have only analysed the words “how to open a bank account” and “in Spain” leading to web pages about bank accounts in Spain, without considering the context of “as a foreigner”.

After the BERT algorithm is introduced, the phrase “as a foreigner” gains importance. The model can understand the links between the words in the query and understand better what the user’s search intent is. Google provides websites about non-residents in Spain opening bank accounts, any visa requirements, etc.

Bidirectionality

It is so important that the BERT model is able to analyse text bidirectionally. This is because 2 sentences with the same word could have completely different meanings to them.

For example, take the sentences “What date is it tomorrow?” and “Good first date ideas”. The word “date” has 2 completely different meanings in these sentences. The first one means what day is it on the calendar, and the other is the act of doing a romantic activity with someone.

It is the context around the word date that allows Google BERT to make sense of what the search intent of the user is. The bidirectional model of analysing text in both directions simultaneously allows this understanding.

Google BERT vs other Google tools

Google Gemini

Google Gemini, formerly known as BARD, is a multimodal model. This means it can understand and analyse different types of information including text, images and code. Gemini is essentially a tool that is built on the foundations of BERT to become a more widely applicable model. It has been designed to process and understand various types of data and has advanced reasoning and understanding. It can be considered a stronger tool than Google BERT, in today’s day and age.

RankBrain

Introduced in 2015, RankBrain is used to help Google better understand the meaning of search queries, in particular, those that may come across as ambiguous or queries that Google is unfamiliar with. This tool focuses more on understanding the general intent of searches, rather than the contextual meaning of words. However, Google BERT and RankBrain do not replace each other. They are complementary tools that work together to improve Google’s overall search results. Both are important parts of the Google search algorithm.

Google USE

Google’s Universal Sentence Encoder is designed to generate sentence-level embeddings. This means that sentences are represented as vectors. The aim is that the semantic meaning of a sentence is captured, from the sentence as a whole. This tool is the best applicable when there is a task where understanding the overall sentence is important. The difference between BERT and USE is that BERT focuses on word-level understanding, while USE focuses on sentence-level understanding.

Advantages of Google BERT

Search engine accuracy

BERT is used by Google to enhance results on the SERPs for search engine users. It is important that results are accurate and relevant for users. BERT helps to ensure this, by understanding the context of search queries and using a natural language processing mechanism.

Improved search query understanding

The bidirectional element of this language understanding tool allows for a deeper analysis of the text. It can understand the context of words in a sentence and the words that come before and after those words. Again, this improves the accuracy of natural language processing tasks and the accuracy of search results.

Adapatble tool

Google BERT is an extremely adaptable and versatile tool. It can be altered to be used for a variety of different tasks, including:

Answering questions. BERT succeeds at answering questions by understanding the context of the question and the text containing the answer.
Semantic analysis. The model can understand the sentiment in a piece of text i.e. if there is a positive, negative or neutral tone.
Classification of text. It can categorise text or content into categories or classes, which is very useful for spam detection, categorising documents, etc.

How does Google BERT impact SEO?

Google BERT has impacted SEO massively since it was implemented into the algorithm back in 2019. It has shifted emphasis from simple keyword matching to the relevance of long-tail keywords and a deeper understanding of contextual meaning and search intent. The model works to understand the nuances of language, which allows Google to better understand conversational or difficult queries. As a result of this, websites with content that contains natural language and context-rich text are being prioritised on SERPs.

Long tail keywords and high-quality content are extremely important in SEO as a result of this. SEO strategies require a particular focus on creating informative, accurate and relevant content that can answer user’s search intents.

Optimising your content with BERT

Use natural language

When writing content for your web pages, ensure that you are using a natural language tone. Write as if you are speaking to a human, not a search engine or robot. Try to avoid having too high of a keyword density and focus more on a conversational and meaningful tone.

Create high-quality content

Provide search engine users with valuable and in-depth content. Your information should fully address the search intent of the user. To do this, you should be consistently optimising your content by improving your structure and readability. Make sure to cite any included sources.

Remember E-E-A-T guidelines

Google guidelines like E-E-A-T can help you. This acronym stands for Experience, Expertise, Authoritativeness and Trustworthiness. Use these guidelines to improve the quality of your content. For example, include the credentials of an author, link to reliable sources, and ensure transparency. Content that falls into the YMYL (Your Money or Your Life) category is particularly impacted by E-E-A-T factors.

Conclusion

Google BERT has been a highly successful algorithm update. It has significantly enhanced the relevance of search results. This allows for a better match with user search intent. Since its release, Google has introduced further updates that build upon BERT’s principles. The natural language processing is now even more refined. Overall, this update has had a profound impact on the SEO world. There has been improvement in how search engines understand queries and provide accurate, relevant results. BERT plays an important role in the optimisation of search engine results for Google users.

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