{"id":111971,"date":"2021-02-17T18:30:00","date_gmt":"2021-02-17T23:30:00","guid":{"rendered":"https:\/\/www.dacgroup.com\/one-year-on-has-bert-really-changed-search-forever-2\/"},"modified":"2024-12-21T05:52:51","modified_gmt":"2024-12-21T10:52:51","slug":"one-year-on-has-bert-really-changed-search-forever-2","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-gb\/insights\/blog\/search-optimization\/one-year-on-has-bert-really-changed-search-forever-2\/","title":{"rendered":"One year on, has BERT really changed search forever?"},"content":{"rendered":"

When we last dived into the burgeoning promise of Google’s BERT AI<\/a>, it was affecting only 10% of all English language searches. But Google always had ambitious plans for BERT’s expansion; plans that saw it roll out BERT across 70 languages in December 2019.<\/p>\n

So, here we are just over a year since BERT got his big break. It’s time to take a peek under the hood to gauge exactly how much Google’s revolutionary AI has actually revolutionised search.<\/p>\n

Remind me… Who is BERT?<\/h2>\n

Although the meaning behind the acronym BERT is rather dry (Bidirectional Encoder Representations from Transformers), BERT is widely considered to be the single most significant search update Google has made since launching RankBrain<\/a> back in 2015.<\/p>\n

BERT’s goal is to better understand conversational language. It places special emphasis on important-but-often-ambiguous prepositions (such as “from” and “to”) that form the common building blocks of language but are notoriously difficult for computers to grasp.<\/p>\n

Examples of BERT in Action<\/h2>\n

Google showed us a live example of just how BERT has helped to improve the results based on the users search intent:<\/p>\n

\"BERT<\/p>\n

Here\u2019s a search for “2019 brazil traveler to usa need a visa”. The word “to”and its relationship to the other words in the query are particularly important to understanding the meaning. It\u2019s about a Brazilian traveling to the U.S., and not the other way around. Previously, our algorithms wouldn’t understand the importance of this connection, and we returned results about U.S. citizens traveling to Brazil. With BERT, Search is able to grasp this nuance and know that the very common word “actually matters a lot here, and we can provide a much more relevant result for this query.<\/p><\/blockquote>\n

In the same post, Google provided more examples of BERT helping them grasp the subtle nuances of language<\/a> that computers have never been able to understand the way humans do:<\/p>\n

Let’s look at another query: “do estheticians stand a lot at work”. Previously, our systems were taking an approach of matching keywords, matching the term “stand-alone” in the result with the word “stand” in the query. But that isn’t the right use of the word “stand” in context. Our BERT models, on the other hand, understand that “stand” is related to the concept of the physical demands of a job, and displays a more useful response.<\/p><\/blockquote>\n

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