Technical SEOPublished02.05.2026

Google BERT

A neural network model for natural language processing that taught Google to understand nuances of speech and prepositions.

In brief

BERT (Bidirectional Encoder Representations from Transformers) is a deep learning method that considers word context from both sides, enabling the search engine to better understand queries with prepositions and complex phrasing.

What is Google BERT

BERT is a neural network architecture that processes text bidirectionally rather than left‑to‑right. This allows Google to consider all words in a query and their relationships, which is critical for understanding prepositions, negations, and word order.

How search worked before BERT

Before BERT, search engines often ignored prepositions and stop words, focusing on main keywords. Example:

TEXT
Query: "ticket to Moscow"
Old approach: matches "ticket" and "Moscow", direction not always considered.
After BERT: the system understands the user wants a ticket from somewhere TO Moscow.

This forced SEOs to move from keyword stuffing to natural, human‑like language.

Impact on SEO

  • Greater focus on search intent rather than exact‑match keywords
  • Increased value of long‑tail, conversational queries
  • Need to write content that answers real user questions, not algorithms
BERT is part of a broader algorithm evolution: Hummingbird → RankBrain → BERT → MUM. Each step improved natural language understanding.
FAQ

Common questions

BERT is applied to both user queries and indexed pages to determine relevance. However, it‘s most actively used at the query understanding stage.
There is no direct optimisation for BERT. Just write clear, logical language that thoroughly answers questions and avoids artificial constructs.
Yes. Since 2020, Google applies BERT to Russian queries, especially noticeable with complex prepositions and phrases like 'how to get to the station'.
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