Google BERT (Bidirectional Encoder Representations from Transformers) is a language model based on the Transformer architecture developed by Google in 2018. BERT is a natural language processing (NLP) technique that uses a two-way approach to understanding the context of words in a text. What makes BERT very powerful is its ability to process language bidirectionally, meaning it can read text from left to right and right to left. This allows BERT to understand the relationship between words in a sentence and how they modify the meaning of other words nearby.
How Google BERT works
Instead of parsing words in a left-to-right or right-to-left sequence, BERT reads words in both directions, allowing you to better capture the context in which they are used. This contextual understanding capability significantly improves performance in NLP tasks such as text classification, machine translation, answering questions, and detecting named entities.
What is Google BERT for?
In 2019, Google announced that it was using BERT in its search algorithm to improve natural language understanding and provide more relevant search results. The inclusion of BERT in Google’s search algorithm has improved the search engine’s ability to interpret complex queries and understand user intent, especially in natural language queries and context-dependent queries.