What is the difference between BERT and RankBrain?

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RankBrain is the natural language processing algorithm that Google has been using since 2015. It is able to compare a search with other similar searches to infer the most accurate meaning possible. RankBrain is applied to the search after it has gone through other traditional algorithms.

Despite its advantages, BERT is not intended to replace RankBrain . Google expects both algorithms to work in tandem. However, for searches that require more subtle interpretation, BERT will do a much more effective job.

While RankBrain can only look at the content before or after a word to make sense of it, BERT can look in both directions to gain a better understanding of the context .

What languages ​​does BERT work in?
Even if the content of your blog or website is in Spanish or another language other than English, Google BERT will still be important for your SEO.

BERT was initially launched only in US English, but Google soon rolled it out to over 70 languages , a list that is sure to continue to grow.

The impact may not reach 10% of all searches for the English version, but it will still be tremendously influential and its effectiveness will most likely continue to grow.

How does Google BERT and natural language processing work?
Human language is extremely complex and subtle. If we sometimes have trouble understanding vp maintenance email list each other, imagine the ingenuity that goes into a program that converts sounds or words into something that a computer can process.

Natural language processing (NLP) algorithms do just that: they convert words (written or spoken) into ones and zeros and create meanings from that. Those meanings can correspond to certain actions or states. NLP is what allows you to communicate with Siri or Alexa, albeit with several limitations.

It’s obvious that NPL is far from perfect. Think about how many times your virtual assistant has failed to understand you! When faced with the task of deriving meaning from context or interpreting an ambiguous sentence—for example, “rose” can refer to the flower, the color, or a name—an NPL algorithm faces a difficult problem. There is a gap between what we want to say and what the algorithm is able to interpret. Google BERT aims to help bridge that gap.

Google BERT, like other NPLs, uses machine learning to teach itself how to solve these problems. It is exposed to millions of human searches, and with each mistake it makes, it tweaks its processes a little to become more effective. To start this learning process, Google relies on human evaluations.

To interpret a search , Google BERT uses many different tools. It is able to classify words according to their syntax: it can distinguish nouns, adjectives, prepositions, etc.

BERT can also identify entities in the search. An entity can be a person, an organism, objects, works, etc. It also performs “sentiment analysis”: it assigns an emotional score to the search and the entities that reflects how positive or negative the user’s attitude and the web pages are. Depending on this score, the results the user sees will be different.