Tuesday, October 29, 2019
Did you know that Google, the uber-popular search engine, makes hundreds, if not thousands of changes to its search algorithm every year? In 2018 alone, they made 3,234 updates to improve the performance of search and deliver relevant results to their users.
Although the majority of the changes are minor, Google also rolls out major algorithmic updates from time to time, such as Panda and Penguin to impact search results in a positive way, displaying more relevant and accurate responses.
Recently, Google has announced the BERT update, which is deemed to be its biggest update in the last five years. This deep learning algorithm will affect one in ten searches and is geared to help Google understand longer, more conversational queries and still produce relevant search results.
To stay on top of providing the best search results, Google continuously improves its natural language understanding capabilities.
People don’t always have the knowledge or process to understand the correct search, and as a result, Google has rolled out the BERT update to understand the human character, nuances, and search intentions and behaviors.
Bidirectional Encoder Representations from Transformers (BERT) is a neural network-based technique for Natural Language Processing (NLP). This technology helps one train their question-answering system.
The BERT model is focused more on understanding the intent behind search queries. The algorithm processes words in the search query in relation to all other words in a sentence, rather than one-by-one in order.
Simply put, BERT is all about comprehending the conversational or sloppy search queries yet returning the right results.
Let’s understand what the BERT model means for users.
Well, in simple words, it will help you find better search results.
With the BERT update, Google can now offer more relevant results for about one in 10 searches in the US in English, and it’ll support other languages and locales in the imminent future.
Further, the new algorithm will facilitate identifying the search queries that have prepositions like “for” and “to” that are more natural for users. It enables users to search more naturally. A lot of testing was conducted before implementing this search model in Google’s algorithm. Here are a few examples to showcase BERT’s ability to understand the intent behind your search.
Here’s a search for “2019 Italy traveler to the USA needs a visa.”
The word “to” and its relationship to the other words in the search query are particularly important to understanding the meaning. It’s about an Italian traveling to the US and not the other way around. Earlier Google’s algorithm wasn’t effective enough to identify this connection and it showed results about US citizens traveling to Brazil.
In other words, we can say that the new BERT technology enables Google to understand the importance of “to” in the query and provide more relevant results to searchers.
A powerful advantage of the BERT model is that it takes the learning from one language and applies it to other languages. This helps Google better understand queries in different languages and offers more relevant results. So far, the BERT model is applied to 24 languages and Google has already started seeing significant improvements in languages like Korean, Hindi, and Portuguese.
With this successful upgrade in the search algorithm, Google still sees a scope for improvement and considers language learning as an ongoing challenge. Their research and development team continuously works on testing and identifying new ways to facilitate search results and you never know we can see a new algorithm upgrade coming up in no time.
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