One of the biggest changes to Google's search system in years has arrived, BERT. BERT stands for Bidirectional Encoder Representations from Transformers. In plain English, this means that BERT is a neural network-based technique for natural language processing pre-training. Still confused? Put as simply as possible, BERT will help Google's search engine understand language a bit more like we humans do. With this new algorithm update, Google will be able to better comprehend the nuances and context of words used in searches and therefore produce more relevant results.
How Does BERT Work?
Described by Google as its "biggest leap forward in the past five years, and one of the biggest leaps forward in the history of search," BERT will be used for both search engine results and featured snippets. Handling tasks such as part of speech tagging, entity recognition, and questions-answering, BERT will enable Google to better understand natural language. The algorithm update will utilize pattern recognition to help Google searches learn how human beings communicate in order to produce more pertinent results.
What Will BERT Change?
BERT will not be replacing RankBrain or any other elements of the existing search algorithm that concentrate on language but working in conjunction with them. It will also have a considerable influence on featured snippets across multiple languages. Although the BERT algorithm update is expected to impact 10% of all searches, it is most likely to affect long-tail searches.
BERT Example
Still unsure about how BERT works and what it will change? Let's take a look at some of the examples provided by Google:
Google's first example involves a search for “2019 brazil traveler to usa need a visa.” The most important part of this example is the word “to” and how it relates to the other words in the query. Google's algorithm needs to understand this search is seeking information about a Brazilian traveling to the U.S., and not the other way around. In the past, search algorithms would not have understood the importance of this connection and would have produced results about U.S. citizens traveling to Brazil. However, now with the help of BERT, Google's search can grasp the context of the very common word “to," and provide a much more relevant result for the query.
Let’s examine another query example: “do estheticians stand a lot at work.” In the past, Google's algorithm would have attempted to match the keyword “stand-alone” in the search engine results with the word “stand” in the query. However, in the context of the search, this is not the right use of the word “stand." The new BERT algorithm update can comprehend that “stand” is actually related to the physical demands of a job, and therefore produce more useful results.
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