Definition
Google BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing (NLP) model integrated into Google's algorithm in October 2019. Its key feature is analyzing words in a sentence in both directions (bidirectional), enabling it to understand context and language nuances with unprecedented precision. BERT impacts approximately 10% of English queries and has been gradually deployed across all languages. It is particularly effective for long, conversational queries where prepositions and connecting words fundamentally change the search meaning.
Key Points
- Bidirectional context analysis of each word
- Particularly impacts long, conversational queries
- Improves featured snippet accuracy
Practical Examples
Decisive preposition
For the query 'travel from Paris to Lyon', BERT understands that the word 'to' indicates the destination, not the starting point, avoiding displaying reversed results.
Improved featured snippets
BERT helps Google select more relevant featured snippets by better understanding complex questions like 'can you take aspirin on an empty stomach'.
Frequently Asked Questions
RankBrain uses machine learning to interpret novel queries, while BERT uses natural language processing to understand the precise context of each word. Both work complementarily within Google's algorithm.
There is no BERT-specific optimization technique. The recommended approach is to write naturally, clearly, and precisely, directly answering user questions without unnecessary jargon.
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Last updated: 2026-02-07