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for "Bidirectional Encoder Representation from Transformers": " Bidirectional " means that BERT analyzes search phrases in both directions, that is, it considers the words to the left and to the right of each keyword. Thus, you can relate all the clipping path service words in the query to each other, instead of considering them individually. " Transformer " refers to a meaning mapping system that helps you understand words like links, pronouns, and prepositions. As we will see in the example below, these words can play a crucial role in interpreting the meaning of a search. By applying these AI and meaning mapping algorithms,
BERT is able to understand the intent behind a natural language query, meaning that it is able to understand the normal way humans express themselves. Keep in mind that BERT is not designed to replace Google's current algorithm (Rank Brain), but rather is a kind of "add-on" to better understand user queries. Therefore, the current Rank Brain, which generates search results lists based on user behavior data, remains in force. How does BERT affect SERPs or Google searches? In the end, BERT is just one more step for Google to fulfill its mission: to quickly give users the exact answers they need . To do this,
they need their algorithms to be able to understand both the content of the pages and user queries. BERT is estimated to affect approximately 1 in 10 searches performed. The main difference is that now the individual keywords are not so much taken into account, but the overall search intent. To illustrate how BERT works, Google uses the example of the search "Brazilian tourist to the US needs a visa". Before, Google ignored the preposition "to" in this search, so it ended up interpreting that it could be an American person who wanted to travel to Brazil. On the other hand, with BERT