Abstract
Search engines are now a need for obtaining information due to the internet's explosive expansion in digital material. One of the most widely used search engines, Google, works hard to improve its search functionality. Google has recently used cutting-edge natural language processing (NLP) methods to enhance search results. The Bidirectional Encoder Representations from Transformers (BERT) method is one such ground-breaking invention. This study seeks to offer a thorough evaluation of the BERT algorithm and its use in Google Search. We examine BERT's design, training procedure, and salient characteristics, emphasising its capacity to comprehend the subtleties and context of real language. We also talk about BERT's effects on user experience and search engine optimisation (SEO), as well as potential future advances and difficulties.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Mathematical Statistician and Engineering Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
