Google has been considered the popular and biggest search engine for more than a decade as it receives and resolves millions of queries in a day.
To make the working of the whole search engine optimization process better and help the users with their queries, Google launches updates from time to time.
The Google Algorithm updates include Panda launched in 2011, Penguin in 2012, Hummingbird in 2013, Mobile Update and RankBrain in 2015, Medic in 2018, and BERT in October 2019 followed by the Core update in 2020 which is an improvement of previous Google updates.
With this blog, we want to help our readers to have a deep understanding of the BERT algorithm update.
BERT stands for Bidirectional Encoder Representations Transformers, which was created to help the search engine in understanding the search queries in a better way and provide the relevant results to the users.
BERT is a natural language processing substructure that is created to understand the words in the search query with the articles in the English language. It works on the concept of machine learning and has been trained for a lot of words including 2500 million words of the English Wikipedia.
Other giants such as Microsoft, Facebook, and SuperGLUE are also making BERT versions.
The Benefits of using BERT for the search engines
Unlimited content is present on the web and as per the English language, many words have the same meaning. Some sentences have a literal meaning and are also used as maxims, which creates a problem for the search engines to understand the correct query made by the user. It is easy for a human to understand the context of the written language but not for the search engines. BERT has made this work easier by understanding the crux of the search query and providing the results.
Usage of BERT models by search engines helps in both ranking and featured snippets. Search engines can perform better by providing better results for the search queries as BERT helps in understanding the context of the language with the prepositions part of the English language. i.e. ‘’To’’,‘’ of’’,‘’ for’’, etc.
BERT model can work with 70 languages, which means it can give us more appropriate results of the search queries made by the user with any language from the several one.
Previously most language models were unidirectional, either they can read the query from the left to right side or from right to the left side. BERT works bi-directional which means it can read the search query from both sides whether it is left or right.
Also BERT model picks the missing word in the search query with other functions such as question answering, automatic summarization, polysemy resolution, named entity determination, etc.
BERT will have a huge impact on voice search and international SEO as well. It will help in solving the ambiguous query and contextual nuance resulting in better results in ranking.
With BERT gaining popularity, an innovation in machine language known as Google SMITH Algorithm is introduced which has the potential to outperform the BERT model.
The main difference between BERT and SMITH algorithm is BERT understands words based on passage while SMITH understands passage based on documents. SMITH helps in better interpretation of the sentence than BERT.
Above we have discussed BERT in detail. Please share your knowledge and experience with us in the comments below!