Figuratively speaking, Google’s search engine is an endless ocean that consists of pretty much anything that you want to know. Be it a complex mathematical equation or any random sports website; Google will have an answer for almost all your questions!
Despite billions of queries being asked on Google Search every day, almost 15% of them are ones that users never asked before. To tackle that issue, Google had rolled in a few updates and improved the results returned after each query.
To explain it in the easiest possible way, Google Search tries to understand and figure out the nuances of different languages when they are entered
Google analyzes the contents of all the web pages available through a process called indexing. After analyzing, Google stores the page information in its database, called Google Index. From the database, Google ranks the web pages based on the queries put forward by the end-user on Google Search. When Google ranks the pages, it factors in all conditions, including the user’s device and location.
What is BERT?
Going by Google Search’s explanation, it is an open-sourced technique based on neural networks. It was introduced to incorporate natural language processing (NLP). BERT (Bidirectional Encoder Representations from Transformers)
So, is BERT a language model? Very much so. BERT has been designed to improve Google Search’s understanding of language, especially conversational queries and general parts of speech that matter
To put in laymen's terms, these BERT language models have helped in customizing results in a way that the user comes across only the most important and matching ones. For now, Google Search using BERT seems like a brand new experience, because 1 in 10 English searches in the U.S.
Coming back to BERT’s modifications on Google Search - even though it is called a bidirectional transformer, it is non-directional. The usual directional models can only read and interpret searches in a single direction (either left-right or right-left). But, through
Another instrumental help from BERT is the ability to interpret queries in one language (preferably English) and then changing it to another language. The model has already seen considerable improvements in Hindi, Korean, and Portuguese.
What lies ahead for the development of BERT? While there are both good and bad things present in BERT’s case, the path they have to choose is not precisely sun-kissed. There are still innumerable cases where Google Search has failed to customize and suit your results, which is when the importance of a technology all-encompassing the language barriers are rolled out!
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