Google Search Now Uses Machine Learning to Organize Top Stories
Google is updating its search algorithm by using automobile learning to surface the most useful and timely articles in the 'Top Stories' department of its search results.
In a blog post on Wednesday, the product manager of Google Search, Duncan Osbourne, said that the new algorithm will help organize the results in a way that will make information technology easier for users to separate the most relevant search results from the tertiary.
Osbourne explained how the new search results will work with the post-obit example: "If you search for 'NASA news', you might encounter results grouped under the distinct news stories 'NASA adds five companies to moon bid' and 'NASA detects water vapor on Jupiter's moon Europa', forth with boosted results under 'Likewise in the news'."
To power this new experience, Google says that information technology's developed a new "story-understanding engineering science" to map the people, places, and things involved in a news story, and and so describe connections between them. To generate these groups, the company says information technology used a variety of auto learning algorithms including a neural network-based technique for natural language processing pre-grooming, called BERT, to examine the related articles and determine where one story ends and another one begins.
The aforementioned update has already started rolling out on mobile devices for English language-linguistic communication searches in the The states and Google says information technology will exist rolled out to more languages and locations in the coming months. It'll exist interesting to see how the new update will touch on publications and their SEO practices, but if Google gets information technology right, information technology could help people gain easier access to the most relevant data and build a deeper understanding of the events around the earth.
Source: https://beebom.com/google-search-organize-top-stories/
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