By Shad Super in Questions —

How Does Google's BERT Update Affect the Evaluation of Content Quality and Relevance in Search Rankings?

Summary

Google's BERT update enhances search rankings by better understanding the context and nuances of natural language in search queries, allowing for more accurate and relevant search results. This algorithm update emphasizes the importance of creating quality content that directly addresses user intent and employs natural language.

Understanding BERT

Google's BERT (Bidirectional Encoder Representations from Transformers) is a deep learning algorithm related to natural language processing (NLP). It allows Google to understand the context of words in a search query rather than processing them individually, thus improving the relevance of search results [Search Language Understanding with BERT, 2019].

Impact on Content Quality Evaluation

With BERT, Google evaluates content quality based on how well it matches the intent behind users' search queries. The algorithm is more adept at understanding the nuances of language, such as the significance of prepositions and the context around query terms [BERT Language Understanding, 2019]. This means that content creators must focus on user-centric content that comprehensively covers topics and answers potential user questions.

Importance of Natural Language

Content must be written in a natural, conversational style that mirrors how people speak and search. This involves using full sentences, subheadings that address specific questions, and sections that provide detailed explanations. By doing so, content is more likely to align with the varied ways users frame their queries [Google BERT: What You Need to Know, 2020].

Relevance to Search Queries

BERT helps Google better match queries with relevant content by understanding the relationships between words in a sentence. For example, in the query "2019 brazil traveler to usa need a visa," BERT helps distinguish the specific need (whether the traveler needs a visa) rather than just focusing on keywords [FAQ: All About the BERT Algorithm, 2019].

Optimizing for BERT

While you can't specifically optimize for BERT as it focuses on user intent, there are best practices that can be followed to ensure content is effective:

  • Target Long-Tail Keywords: These reflect more specific user intents and are better understood by BERT.
  • Create Comprehensive Content: Cover topics thoroughly to address the multiple facets of a query.
  • Use Structured Data: While BERT doesn't rely on structured data, using it can improve search result appearance.

Examples of BERT in Action

Consider the search query "can you get medicine for someone pharmacy." Pre-BERT, Google might have matched this to generic results about pharmacies. With BERT, the engine understands the query's intent in context, providing results about policies on picking up prescriptions for others [BERT for Search, 2019].

Conclusion

Google's BERT update represents a significant advancement in how search engines process natural language, facilitating more accurate and contextually relevant search results. This necessitates content that is deeply aligned with user intent, comprehensive in scope, and written in a natural, conversational style.

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