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“Wouldn’t it be nice if Google understood the meaning of your phrase, rather than just the words that are in the phrase?” said Google’s Eric Schmidt back in March 2009, just before the company announced rolling out their first semantic offerings.This signaled one of the first moves away from “strings to things,” and is perhaps the advent of entity-oriented search implementation by Google.

On the whole, however, much of language can be resolved by mathematical computations around where words live together (the company they keep), and this forms a large part of how search engines are beginning to resolve natural language challenges (including the BERT update).

Google’s team of linguists (Google Pygmalion) working on Google Assistant, for example, in 2016 was made up of around 100 Ph.D. linguists.

By 2019, the Pygmalion team was an army of 200 linguists around the globe

BERT in search is mostly about resolving linguistic ambiguity in natural language. BERT provides text-cohesion which comes from often the small details in a sentence that provides structure and meaning

BERT is not an algorithmic update like Penguin or Panda since BERT does not judge web pages either negatively or positively, but more improves the understanding of human language for Google search.  As a result, Google understands much more about the meaning of content on pages it comes across and also the queries users issue taking word’s full context into consideration.

BERT is about sentences and phrases

We may see this reduction in recall reflected in the number of impressions we see in Google Search Console, particularly for pages with long-form content which might currently be in recall for queries they are not particularly relevant for.

International SEO may benefit dramatically too

Question and answering directly in SERPs will likely continue to get more accurate which could lead to a further reduction in click through to sites.

Can you optimize your SEO for BERT?Probably not.The inner workings of BERT are complex and multi-layered.  So much so, there is now even a field of study called “Bertology” which has been created by the team at Hugging Face.It is highly unlikely any search engineer questioned could explain the reasons why something like BERT would make the decisions it does with regards to rankings (or anything).Furthermore, since BERT can be fine-tuned across parameters and multiple weights then self-learns in an unsupervised feed-forward fashion, in a continual loop, it is considered a black-box algorithm. A form of unexplainable AI.BERT is thought to not always know why it makes decisions itself. How are SEOs then expected to try to “optimize” for it?BERT is designed to understand natural language so keep it natural.We should continue to create compelling, engaging, informative and well-structured content and website architectures in the same way you would write, and build sites, for humans.

What is neural matching? Google explained “Neural matching is an AI-based system Google began using in 2018 primarily to understand how words are related to concepts.”“It’s like a super synonym system. Synonyms are words that are closely related to other words,” Google added.