BERT (Bidirectional Encoder Representations from Transformers) is a recent paper published by researchers at Google AI Language
Google said this update will affect complicated search queries that depend on context.
According to Google:
“These improvements are oriented around improving language understanding, particularly for more natural language/conversational queries, as BERT is able to help Search better understand the nuance and context of words in Searches and better match those queries with helpful results.
Particularly for longer, more conversational queries, or searches where prepositions like “for” and “to” matter a lot to the meaning, Search will be able to understand the context of the words in your query. You can search in a way that feels natural for you.”
What is BERT Algorithm?
Search algorithm patent expert Bill Slawski (@bill_slawski of @GoFishDigital) described BERT like this:
“Bert is a natural language processing pre-training approach that can be used on a large body of text. It handles tasks such as entity recognition, part of speech tagging, and question-answering among other natural language processes. Bert helps Google understand natural language text from the Web.
Google has open sourced this technology, and others have created variations of BERT.”
The BERT algorithm (Bidirectional Encoder Representations from Transformers) is a deep learning algorithm related to natural language processing. It helps a machine to understand what words in a sentence mean, but with all the nuances of context.
How BERT works?
Last year, we introduced and open-sourced a neural network-based technique for natural language processing (NLP) pre-training called Bidirectional Encoder Representations from Transformers, or as we call it–BERT, for short. This technology enables anyone to train their own state-of-the-art question answering system.
This breakthrough was the result of Google research on transformers: models that process words in relation to all the other words in a sentence, rather than one-by-one in order. BERT models can therefore consider the full context of a word by looking at the words that come before and after it—particularly useful for understanding the intent behind search queries.
But it’s not just advancements in software that can make this possible: we needed new hardware too. Some of the models we can build with BERT are so complex that they push the limits of what we can do using traditional hardware, so for the first time we’re using the latest Cloud TPUs to serve search results and get you more relevant information quickly.
For more you can read from google’s office website: Understanding searches better than ever before