{"id":29861,"date":"2020-01-15T09:24:35","date_gmt":"2020-01-14T20:24:35","guid":{"rendered":"https:\/\/pureseotest.wpengine.com\/?p=29861"},"modified":"2020-01-15T15:31:28","modified_gmt":"2020-01-15T02:31:28","slug":"google-bert-update","status":"publish","type":"post","link":"https:\/\/pureseo.com\/cn\/blog\/google-blog\/google-bert-update","title":{"rendered":"Google’s BERT Update: What You Need to Know & How to Optimise Your Content"},"content":{"rendered":"\n

On the 21st<\/sup> of October 2019, Google\u2019s search engine rolled out its latest update for English language queries: the Bidirectional Encoder Representations from Transformers (BERT)<\/a> algorithm. It impacts approximately 1 out of 10 queries\u2014the informational queries in particular. If it hasn’t impacted your site yet, rest assured it eventually will as your traffic grows.<\/p>\n\n\n\n

So, what is BERT, how does it work, and what can you do? Here’s everything you need to know about BERT, and how you can improve your site’s content to accommodate one of the biggest Google algorithm updates to date!<\/p>\n\n\n\n
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What is BERT? <\/h2>\n\n\n\n

Unlike previous algorithms, BERT was created to have better Natural Language Processing (NLP). This is the way in which computers understand and communicate with humans and a component of Artificial Intelligence (AI). BERT is both the first fully trained language model and the first bidirectional contextual language model, meaning that it is the most significant breakthrough model in NLP so far. <\/p>\n\n\n\n

In short, BERT helps improve Search by helping Google understand the connotation of a question to better match questions with related answers. <\/strong><\/p>\n\n\n\n
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How does BERT work?<\/h2>\n\n\n\n

Written words are ambiguous and polysemantic (i.e. has many meanings). When spoken, different tones inflected can also result in different meanings. In addition, people tend to type in incomplete sentences without proper punctuation according to how they think. BERT provides context and helps Google better understand human language in conversational English.<\/p>\n\n\n\n

Let’s look at an example given by Google. In this search for \u201c2019 brazil traveler to usa need a visa\u201d, the word \u201cto\u201d is critical to understanding the question as it is about someone travelling from Brazil to the US, instead of the other way around. With BERT, the algorithm understands the intent behind this statement, and the results produced are much more relevant to the query than before.  <\/p>\n\n\n\n

\"Mobile
Google’s Before and After results after implementing BERT<\/em><\/figcaption><\/figure><\/div>\n\n\n\n
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Another example would be this search for \u201cdo estheticians stand a lot at work\u201d, with the focus of the question being on the physical demands of the job. Previously, the engine understood and matched \u201cstand\u201d to the term \u201cstand-alone\u201d, which was not the proper use of the word in the context of which it was asked. With BERT, the meaning behind the query is understood, and much more useful responses are displayed instead. <\/p>\n\n\n\n

\"Mobile
Google’s Before and After results after implementing BERT<\/em> <\/figcaption><\/figure><\/div>\n\n\n\n
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Previously, all language models could only move from left to right or right to left in one direction, not both at the same time. <\/p>\n\n\n\n

\"Uni-directional
Uni-directional language modeller <\/em> <\/figcaption><\/figure><\/div>\n\n\n\n
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As the first <\/em>bi-directional contextual language model, BERT processes words in relation to other words in a sentence, instead of looking at each word in order, helping provide much better context to a query. <\/p>\n\n\n\n

\"Bi-directional
Bi-directional language modeller<\/em> <\/figcaption><\/figure><\/div>\n\n\n\n
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What does the BERT update mean for content optimisation?<\/h2>\n\n\n\n