{"id":837,"date":"2019-10-25T11:28:00","date_gmt":"2019-10-25T11:28:00","guid":{"rendered":"https:\/\/www.greenfintech.com\/blog\/?p=837"},"modified":"2022-10-13T11:34:12","modified_gmt":"2022-10-13T11:34:12","slug":"understanding-searches-better-than-ever-before","status":"publish","type":"post","link":"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/","title":{"rendered":"Understanding searches better than ever before"},"content":{"rendered":"\n

If there\u2019s one thing I\u2019ve learned over the 15 years working on Google Search, it\u2019s that people\u2019s curiosity is endless. We see billions of searches every day, and 15 percent of those queries are ones we haven\u2019t seen before–so we\u2019ve built ways to return results for queries we can\u2019t anticipate.<\/p>\n\n\n\n

When people like you or I come to Search, we aren\u2019t always quite sure about the best way to formulate a query. We might not know the right words to use, or how to spell something, because often times, we come to Search looking to learn–we don\u2019t necessarily have the knowledge to begin with.<\/p>\n\n\n\n

At its core, Search is about understanding language. It\u2019s our job to figure out what you\u2019re searching for and surface helpful information from the web, no matter how you spell or combine the words in your query. While we\u2019ve continued to improve our language understanding capabilities over the years, we sometimes still don\u2019t quite get it right, particularly with complex or conversational queries. In fact, that\u2019s one of the reasons why people often use \u201ckeyword-ese,\u201d typing strings of words that they think we\u2019ll understand, but aren\u2019t actually how they\u2019d naturally ask a question.<\/p>\n\n\n\n

With the latest advancements from our research team in the science of language understanding–made possible by machine learning–we\u2019re making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search.<\/p>\n\n\n\n

Applying BERT models to Search<\/h2>\n\n\n\n

Last year, we introduced and open-sourced a neural network-based technique for natural language processing (NLP) pre-training called Bidirectional Encoder Representations from Transformers, or as we call it–BERT, for short. This technology enables anyone to train their own state-of-the-art question answering system.<\/p>\n\n\n\n

This breakthrough was the result of Google research on transformers: models that process words in relation to all the other words in a sentence, rather than one-by-one in order. BERT models can therefore consider the full context of a word by looking at the words that come before and after it\u2014particularly useful for understanding the intent behind search queries.<\/p>\n\n\n\n

But it\u2019s not just advancements in software that can make this possible: we needed new hardware too. Some of the models we can build with BERT are so complex that they push the limits of what we can do using traditional hardware, so for the first time we\u2019re using the latest Cloud TPUs to serve search results and get you more relevant information quickly.<\/p>\n\n\n\n

Cracking your queries<\/h2>\n\n\n\n

So that\u2019s a lot of technical details, but what does it all mean for you? Well, by applying BERT models to both ranking and featured snippets in Search, we\u2019re able to do a much better job helping you find useful information. In fact, when it comes to ranking results, BERT will help Search better understand one in 10 searches in the U.S. in English, and we\u2019ll bring this to more languages and locales over time.<\/p>\n\n\n\n

Particularly for longer, more conversational queries, or searches where prepositions like \u201cfor\u201d and \u201cto\u201d matter a lot to the meaning, Search will be able to understand the context of the words in your query. You can search in a way that feels natural for you.<\/p>\n\n\n\n

To launch these improvements, we did a lot of testing to ensure that the changes actually are more helpful. Here are some of the examples that showed up our evaluation process that demonstrate BERT\u2019s ability to understand the intent behind your search.<\/p>\n\n\n\n

Here\u2019s a search for \u201c2019 brazil traveler to usa need a visa.\u201d The word \u201cto\u201d and its relationship to the other words in the query are particularly important to understanding the meaning. It\u2019s about a Brazilian traveling to the U.S., and not the other way around. Previously, our algorithms wouldn’t understand the importance of this connection, and we returned results about U.S. citizens traveling to Brazil. With BERT, Search is able to grasp this nuance and know that the very common word \u201cto\u201d actually matters a lot here, and we can provide a much more relevant result for this query.<\/p>\n\n\n

\n
\"Query-2019BrazilTravelerToUSANeedAVisa.max-1000x1000\"<\/a><\/figure>\n<\/div>\n\n\n

Let\u2019s look at another query: \u201cdo estheticians stand a lot at work.\u201d Previously, our systems were taking an approach of matching keywords, matching the term \u201cstand-alone\u201d in the result with the word \u201cstand\u201d in the query. But that isn\u2019t the right use of the word \u201cstand\u201d in context. Our BERT models, on the other hand, understand that \u201cstand\u201d is related to the concept of the physical demands of a job, and displays a more useful response.<\/p>\n\n\n

\n
\"Query-DoEstheticiansStandALotAtWork.max-1000x1000\"<\/a><\/figure>\n<\/div>\n\n\n

Here are some other examples where BERT has helped us grasp the subtle nuances of language that computers don\u2019t quite understand the way humans do.<\/p>\n\n\n

\n
\"Query-CanYouGetMedicineForSomeonePharmacy.max-2000x2000\"<\/a><\/figure>\n<\/div>\n\n\n

With the BERT model, we can better understand that \u201cfor someone\u201d is an important part of this query, whereas previously we missed the meaning, with general results about filling prescriptions.<\/p>\n\n\n

\n
\"GUBGMHVpwcTsPFvNNVdG5DKtpv6UwksAfzw5aULcGzgG\"<\/a><\/figure>\n<\/div>\n\n\n

In the past, a query like this would confuse our systems–we placed too much importance on the word \u201ccurb\u201d and ignored the word \u201cno\u201d, not understanding how critical that word was to appropriately responding to this query. So we\u2019d return results for parking on a hill with a curb!<\/p>\n\n\n

\n
\"Query-MathPracticeBooksForAdults.max-2000x2000\"<\/a><\/figure>\n<\/div>\n\n\n

While the previous results page included a book in the \u201cYoung Adult\u201d category, BERT can better understand that \u201cadult\u201d is being matched out of context, and pick out a more helpful result.<\/p>\n\n\n\n

Improving Search in more languages<\/h2>\n\n\n\n

We\u2019re also applying BERT to make Search better for people across the world. A powerful characteristic of these systems is that they can take learnings from one language and apply them to others. So we can take models that learn from improvements in English (a language where the vast majority of web content exists) and apply them to other languages. This helps us better return relevant results in the many languages that Search is offered in.<\/p>\n\n\n\n

For featured snippets, we\u2019re using a BERT model to improve featured snippets in the two dozen countries where this feature is available, and seeing significant improvements in languages like Korean, Hindi and Portuguese.<\/p>\n\n\n\n

Search is not a solved problem<\/h2>\n\n\n\n

No matter what you\u2019re looking for, or what language you speak, we hope you\u2019re able to let go of some of your keyword-ese and search in a way that feels natural for you. But you\u2019ll still stump Google from time to time. Even with BERT, we don\u2019t always get it right. If you search for \u201cwhat state is south of Nebraska,\u201d BERT\u2019s best guess is a community called \u201cSouth Nebraska.\u201d (If you’ve got a feeling it’s not in Kansas, you’re right.)<\/p>\n\n\n\n

Language understanding remains an ongoing challenge, and it keeps us motivated to continue to improve Search. We\u2019re always getting better and working to find the meaning in– and most helpful information for– every query you send our way.<\/p>\n","protected":false},"excerpt":{"rendered":"

If there\u2019s one thing I\u2019ve learned over the 15 years working on Google Search, it\u2019s that people\u2019s curiosity is endless.<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[25],"class_list":["post-837","post","type-post","status-publish","format-standard","hentry","category-web-marketing","tag-seo"],"yoast_head":"\nUnderstanding searches better than ever before - Tech Blog!<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Understanding searches better than ever before - Tech Blog!\" \/>\n<meta property=\"og:description\" content=\"If there\u2019s one thing I\u2019ve learned over the 15 years working on Google Search, it\u2019s that people\u2019s curiosity is endless.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/\" \/>\n<meta property=\"og:site_name\" content=\"Tech Blog!\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/greenfintech\/\" \/>\n<meta property=\"article:published_time\" content=\"2019-10-25T11:28:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-10-13T11:34:12+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.greenfintech.com\/blog\/wp-content\/uploads\/2022\/10\/Query-2019BrazilTravelerToUSANeedAVisa.max-1000x1000-1.jpg\" \/>\n<meta name=\"author\" content=\"Ilyas Malik\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ilyas Malik\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/\"},\"author\":{\"name\":\"Ilyas Malik\",\"@id\":\"https:\/\/www.greenfintech.com\/blog\/#\/schema\/person\/86ad9dad49f15e4ab4dcd0cf91f221a7\"},\"headline\":\"Understanding searches better than ever before\",\"datePublished\":\"2019-10-25T11:28:00+00:00\",\"dateModified\":\"2022-10-13T11:34:12+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/\"},\"wordCount\":1175,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.greenfintech.com\/blog\/#organization\"},\"keywords\":[\"SEO\"],\"articleSection\":[\"Web Marketing\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/\",\"url\":\"https:\/\/www.greenfintech.com\/blog\/understanding-searches-better-than-ever-before\/\",\"name\":\"Understanding searches better than ever before - 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