BERT (Bidirectional Encoder Representations from Transformers)

BERT (Bidirectional Encoder Representations from Transformers) is a machine learning model that is used by search engines like Google to better understand the context and meaning behind search queries.

It works by analyzing not just the individual words in a search query, but also the relationships between them. This allows search engines to better understand the intent behind a query and provide more accurate and relevant search results.

For example, if someone searches for “lawyer for car accident”, BERT can help the search engine understand that the user is looking for a lawyer to help with a car accident case, rather than just any lawyer or information about car accidents.

As a legal SEO expert, understanding BERT and how it affects search results is crucial for optimizing your website’s content and improving your search engine rankings. By creating high-quality content that accurately and clearly answers your audience’s search queries, you can improve your chances of appearing at the top of search results and attracting more potential clients to your law firm.