{"id":8168,"date":"2026-10-13T07:30:00","date_gmt":"2026-10-13T05:30:00","guid":{"rendered":"https:\/\/taismo.de\/?p=8168"},"modified":"2026-10-12T12:05:01","modified_gmt":"2026-10-12T10:05:01","slug":"google-bert","status":"publish","type":"post","link":"https:\/\/taismo.de\/en\/what-is\/google-bert\/","title":{"rendered":"Google BERT"},"content":{"rendered":"<style>\n.taismo-art{display:grid;grid-template-columns:320px minmax(0,1fr);gap:36px;align-items:start}\n.taismo-art .toc-sticky{position:sticky;top:80px}\n.taismo-art .toc-sticky ol li{margin-bottom:9px}\n.taismo-art .toc-sticky ol a:hover{color:var(--toc-hover) !important}\n.taismo-art .taismo-main h2{scroll-margin-top:90px;margin-top:38px}\n.taismo-art .lead-def{font-size:1.15em;line-height:1.6;background:#f7f2ec;border-left:4px solid #ED8924;padding:16px 20px;border-radius:8px}\n.taismo-art table{border-collapse:collapse;width:100%;margin:18px 0}\n.taismo-art th,.taismo-art td{border:1px solid #d9e0df;padding:9px 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<\/g> <\/svg><\/div>\n<h3 style=\"text-align:center;color:#fff;margin:0 0 14px;font-size:1.1em\">Contents<\/h3>\n<ol style=\"margin:0;padding-left:1.2em;line-height:1.5;font-weight:600;color:#fff;font-size:0.9em\">\n<li><a href=\"#term-profile\" style=\"color:#fff;text-decoration:none\">Term profile<\/a><\/li>\n<li><a href=\"#how-bert-works\" style=\"color:#fff;text-decoration:none\">How does Google BERT work?<\/a><\/li>\n<li><a href=\"#bert-in-search\" style=\"color:#fff;text-decoration:none\">What did BERT change in Google Search?<\/a><\/li>\n<li><a href=\"#bert-today\" style=\"color:#fff;text-decoration:none\">Does Google still use BERT today?<\/a><\/li>\n<li><a href=\"#bert-vs-gpt\" style=\"color:#fff;text-decoration:none\">What is the difference between BERT and GPT?<\/a><\/li>\n<li><a href=\"#optimizing-for-bert\" style=\"color:#fff;text-decoration:none\">Can you optimize for Google BERT?<\/a><\/li>\n<li><a href=\"#faq\" style=\"color:#fff;text-decoration:none\">Frequently asked questions about Google BERT<\/a><\/li>\n<\/ol>\n<\/aside>\n<div class=\"taismo-main\">\n<h1 class=\"brand-claim\">What is Google BERT?<\/h1>\n<p class=\"lead-def\"><strong>Google BERT<\/strong> is a language model from Google that works out the meaning of a word from the context of the words before and after it. The name stands for Bidirectional Encoder Representations from Transformers. Google has been using BERT in Search since October 2019 to better understand search queries and text passages, especially long queries in natural language.<\/p>\n<figure>\n      <img decoding=\"async\" src=\"https:\/\/taismo.de\/wp-content\/uploads\/2026\/10\/google-bert-sprachmodell-suche.webp\" alt=\"Google BERT explained: the language model Bidirectional Encoder Representations from Transformers in Google Search, taismo SEO glossary\" title=\"Google BERT: how Google understands words in context\" width=\"1200\" height=\"675\" loading=\"lazy\" style=\"width:100%;height:auto;border-radius:12px\" \/><br \/>\n    <\/figure>\n<h2 id=\"term-profile\">Term profile<\/h2>\n<table>\n<tr>\n<th>Attribute<\/th>\n<th>Details<\/th>\n<\/tr>\n<tr>\n<td>Part of speech<\/td>\n<td>Proper noun, acronym<\/td>\n<\/tr>\n<tr>\n<td>Full name<\/td>\n<td>Bidirectional Encoder Representations from Transformers<\/td>\n<\/tr>\n<tr>\n<td>Pronunciation<\/td>\n<td>b\u025c\u02d0rt<\/td>\n<\/tr>\n<tr>\n<td>Developer<\/td>\n<td>Google AI Language, research paper by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova<\/td>\n<\/tr>\n<tr>\n<td>Released<\/td>\n<td>October 2018 as a research paper, November 2018 as open source<\/td>\n<\/tr>\n<tr>\n<td>In Google Search<\/td>\n<td>since October 25, 2019 for US English, since December 2019 in more than 70 languages, including German<\/td>\n<\/tr>\n<tr>\n<td>Model sizes<\/td>\n<td>BERT-Base with 110 million, BERT-Large with 340 million parameters<\/td>\n<\/tr>\n<tr>\n<td>Related terms<\/td>\n<td>RankBrain, MUM, transformer, natural language processing, semantic search<\/td>\n<\/tr>\n<\/table>\n<h2 id=\"how-bert-works\">How does Google BERT work?<\/h2>\n<p><strong>Google BERT reads every word together with its left and its right context at the same time and derives what the word means in exactly this sentence.<\/strong> Earlier language models mostly processed text in one direction, from left to right. The &#8220;B&#8221; in BERT stands for this bidirectional reading, which builds on the transformer architecture from 2017.<\/p>\n<p>BERT learned this in two phases:<\/p>\n<ol>\n<li><strong>Pre-training:<\/strong> The model read the English Wikipedia with around 2.5 billion words and a collection of books with around 800 million words. In the process, 15 percent of the words were hidden, and BERT had to guess them from the surrounding text. A second exercise was to recognize whether two sentences follow each other in the original.<\/li>\n<li><strong>Fine-tuning:<\/strong> The pre-trained model is then tailored to a specific task with a small amount of additional training, such as answering questions or classifying texts.<\/li>\n<\/ol>\n<p>The result is a model that does not know words as fixed entries but represents them differently depending on the sentence. Technically, BERT creates an <a href=\"https:\/\/taismo.de\/en\/what-is\/embedding\/\">embedding<\/a> for every word, a vector of numbers that captures its meaning in context. The word &#8220;bank&#8221; gets a different vector next to &#8220;withdraw money&#8221; than next to &#8220;by the river&#8221;.<\/p>\n<figure>\n      <a href=\"https:\/\/taismo.de\/en\/seo-services\/\" title=\"SEO services by taismo\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 820 360\" role=\"img\" aria-labelledby=\"abb1t abb1d\" style=\"width:100%;height:auto;max-width:820px;font-family:Arial,Helvetica,sans-serif\"><title id=\"abb1t\">How Google BERT reads the word bank from both directions<\/title><desc id=\"abb1d\">Two example sentences contain the same word bank, highlighted in orange. In the sentence I withdraw money at the bank today, arcs lead from all words on the left and right to bank, and the result is the financial meaning. In the sentence We sit on the bank by the river, the same two-sided view leads to the meaning riverbank. A note below explains that earlier models read only from left to right while BERT uses both sides at once.<\/desc><rect x=\"0\" y=\"0\" width=\"820\" height=\"360\" fill=\"#ffffff\"\/><text x=\"8\" y=\"30\" fill=\"#072328\" font-size=\"16\" font-weight=\"bold\">One word, two meanings: BERT reads in both directions<\/text><path d=\"M35 108 Q202 64 369 106\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M105 108 Q237 64 369 106\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M192 108 Q280 64 369 106\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M254 108 Q311 64 369 106\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M307 108 Q338 64 369 106\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M439 108 Q404 64 369 106\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><rect x=\"20\" y=\"110\" width=\"30\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"35\" y=\"133\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">I<\/text><rect x=\"60\" y=\"110\" width=\"90\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"105\" y=\"133\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">withdraw<\/text><rect x=\"160\" y=\"110\" width=\"65\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"192\" y=\"133\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">money<\/text><rect x=\"235\" y=\"110\" width=\"39\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"254\" y=\"133\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">at<\/text><rect x=\"284\" y=\"110\" width=\"47\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"307\" y=\"133\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">the<\/text><rect x=\"341\" y=\"110\" width=\"56\" height=\"36\" rx=\"8\" fill=\"#ED8924\" stroke=\"#ED8924\" stroke-width=\"2\"\/><text x=\"369\" y=\"133\" text-anchor=\"middle\" fill=\"#ffffff\" font-size=\"14\" font-weight=\"bold\">bank<\/text><rect x=\"407\" y=\"110\" width=\"65\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"439\" y=\"133\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">today<\/text><path d=\"M486 128 L512 128\" stroke=\"#072328\" stroke-width=\"2\"\/><path d=\"M520 128 L510 122 L510 134 Z\" fill=\"#072328\"\/><rect x=\"526\" y=\"110\" width=\"170\" height=\"36\" rx=\"18\" fill=\"#072328\"\/><text x=\"611\" y=\"133\" text-anchor=\"middle\" fill=\"#ffffff\" font-size=\"13\" font-weight=\"bold\">Meaning: finance<\/text><path d=\"M39 218 Q149 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M92 218 Q176 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M145 218 Q202 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M198 218 Q229 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M317 218 Q288 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M370 218 Q315 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><path d=\"M436 218 Q348 174 260 216\" fill=\"none\" stroke=\"#ED8924\" stroke-width=\"1.6\" opacity=\"0.75\"\/><rect x=\"20\" y=\"220\" width=\"39\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"39\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">We<\/text><rect x=\"69\" y=\"220\" width=\"47\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"92\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">sit<\/text><rect x=\"126\" y=\"220\" width=\"39\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"145\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">on<\/text><rect x=\"175\" y=\"220\" width=\"47\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"198\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">the<\/text><rect x=\"232\" y=\"220\" width=\"56\" height=\"36\" rx=\"8\" fill=\"#ED8924\" stroke=\"#ED8924\" stroke-width=\"2\"\/><text x=\"260\" y=\"243\" text-anchor=\"middle\" fill=\"#ffffff\" font-size=\"14\" font-weight=\"bold\">bank<\/text><rect x=\"298\" y=\"220\" width=\"39\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"317\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">by<\/text><rect x=\"347\" y=\"220\" width=\"47\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"370\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">the<\/text><rect x=\"404\" y=\"220\" width=\"65\" height=\"36\" rx=\"8\" fill=\"#ffffff\" stroke=\"#072328\" stroke-width=\"2\"\/><text x=\"436\" y=\"243\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"14\">river<\/text><path d=\"M483 238 L509 238\" stroke=\"#072328\" stroke-width=\"2\"\/><path d=\"M517 238 L507 232 L507 244 Z\" fill=\"#072328\"\/><rect x=\"523\" y=\"220\" width=\"170\" height=\"36\" rx=\"18\" fill=\"#072328\"\/><text x=\"608\" y=\"243\" text-anchor=\"middle\" fill=\"#ffffff\" font-size=\"13\" font-weight=\"bold\">Meaning: riverbank<\/text><rect x=\"20\" y=\"276\" width=\"640\" height=\"52\" rx=\"12\" fill=\"#f4f6f6\"\/><text x=\"340\" y=\"298\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"13\" font-weight=\"bold\">Earlier models read only from left to right.<\/text><text x=\"340\" y=\"317\" text-anchor=\"middle\" fill=\"#072328\" font-size=\"12\">BERT evaluates the left and the right context at the same time.<\/text><g opacity=\"0.08\" transform=\"translate(705,250) scale(0.085)\"><path fill=\"#072328\" 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x=\"812\" y=\"350\" text-anchor=\"end\" fill=\"#8a9695\" font-size=\"11\">Fig. 1 \u00b7 taismo<\/text><\/svg><\/a><figcaption><strong>Fig. 1: One word, two meanings. Google BERT derives the meaning of &#8220;bank&#8221; from the left and the right context at the same time.<\/strong><\/figcaption><\/figure>\n<h2 id=\"bert-in-search\">What did BERT change in Google Search?<\/h2>\n<p><strong>With BERT, Google understands small words such as &#8220;for&#8221;, &#8220;to&#8221;, or &#8220;without&#8221; that change the meaning of a search query.<\/strong> Google announced the rollout on October 25, 2019 and called it the biggest leap forward of the past five years. At launch, BERT affected about one in ten search queries in English in the US. A year later, Google said it was using BERT for almost every English-language query.<\/p>\n<p>Google explained the effect with its own examples:<\/p>\n<ul>\n<li><strong>&#8220;2019 brazil traveler to usa need a visa&#8221;:<\/strong> Before BERT, Search overlooked the word &#8220;to&#8221; and showed results for US citizens traveling to Brazil. With BERT, it recognized that a traveler from Brazil is looking for a visa for the US.<\/li>\n<li><strong>&#8220;can you get medicine for someone pharmacy&#8221;:<\/strong> With BERT, Search grasped that the query is about picking up medicine for another person and not about prescriptions in general.<\/li>\n<\/ul>\n<p>In addition, Google started using BERT for <a href=\"https:\/\/taismo.de\/en\/what-is\/featured-snippet\/\">featured snippets<\/a> in 2019, in all of the roughly two dozen countries where they existed at the time. Long queries that sound spoken benefited most, precisely the <a href=\"https:\/\/taismo.de\/en\/what-is\/longtail-keyword\/\">longtail keywords<\/a> in which every single word counts. The <a href=\"https:\/\/taismo.de\/en\/what-is\/search-intent\/\">search intent<\/a> of such queries often only becomes clear from their prepositions.<\/p>\n<h2 id=\"bert-today\">Does Google still use BERT today?<\/h2>\n<p><strong>Yes, Google still lists BERT as an active system in its official guide to Google Search ranking systems.<\/strong> There, Google describes BERT as an AI system that understands how combinations of words express different meanings and intents. During the US government&#8217;s antitrust case against Google, it became known in 2023 that Google internally calls the use of BERT in ranking DeepRank.<\/p>\n<p>BERT does not work alone. RankBrain was added back in 2015, the Multitask Unified Model (MUM) followed in 2021, and today models from the Gemini family write the answers in <a href=\"https:\/\/taismo.de\/en\/what-is\/ai-overviews\/\">AI Overviews<\/a>. Together, these systems form the foundation of <a href=\"https:\/\/taismo.de\/en\/what-is\/semantic-search\/\">semantic search<\/a>.<\/p>\n<p>Outside Google, BERT is one of the most influential language models of all. Numerous offshoots are based on its architecture, such as RoBERTa from Meta, DistilBERT from Hugging Face, and Sentence-BERT, which encodes sentences so they can be compared by meaning. For the German language, the Berlin company deepset released its own BERT model in 2019.<\/p>\n<h2 id=\"bert-vs-gpt\">What is the difference between BERT and GPT?<\/h2>\n<p><strong>BERT is designed to understand language, GPT to generate it.<\/strong> Both model families are based on the transformer architecture but use different halves of it.<\/p>\n<table>\n<tr>\n<th>Attribute<\/th>\n<th>BERT<\/th>\n<th>GPT<\/th>\n<\/tr>\n<tr>\n<td>Developer<\/td>\n<td>Google<\/td>\n<td>OpenAI<\/td>\n<\/tr>\n<tr>\n<td>Architecture<\/td>\n<td>Encoder<\/td>\n<td>Decoder<\/td>\n<\/tr>\n<tr>\n<td>Reading direction<\/td>\n<td>Both ways, left and right context at the same time<\/td>\n<td>Left to right, word by word<\/td>\n<\/tr>\n<tr>\n<td>Strength<\/td>\n<td>Understanding, classifying, and matching texts<\/td>\n<td>Writing new texts<\/td>\n<\/tr>\n<tr>\n<td>Typical use<\/td>\n<td>Understanding search queries and passages in Search<\/td>\n<td>Writing answers in chat and AI systems<\/td>\n<\/tr>\n<tr>\n<td>Size<\/td>\n<td>Up to 340 million parameters (2018)<\/td>\n<td>GPT-3 with 175 billion parameters (2020)<\/td>\n<\/tr>\n<\/table>\n<p>Modern <a href=\"https:\/\/taismo.de\/en\/what-is\/llm\/\">large language models<\/a> such as GPT or Gemini are many times larger than BERT. For quickly understanding and matching millions of search queries, compact encoder models like BERT nevertheless remain in use.<\/p>\n<h2 id=\"optimizing-for-bert\">Can you optimize for Google BERT?<\/h2>\n<p><strong>No, there is no separate optimization for BERT.<\/strong> Danny Sullivan, Search Liaison at Google at the time, made it clear in October 2019 that there is nothing to optimize for with BERT and that the fundamentals remain unchanged: Google wants to reward helpful content. BERT does shift which texts benefit, however. This leads to 4 consequences:<\/p>\n<ol>\n<li><strong>Write naturally:<\/strong> A text that answers a question in normal language fits a model that understands normal language. Strings of search terms such as &#8220;dentist munich cheap fast&#8221; do not help and are closer to a case of <a href=\"https:\/\/taismo.de\/en\/what-is\/keyword-stuffing\/\">keyword stuffing<\/a>.<\/li>\n<li><strong>Answer the question precisely:<\/strong> When small words determine the meaning, the page that answers exactly the question asked wins, not a related one.<\/li>\n<li><strong>Take long queries seriously:<\/strong> Since BERT, Google can match specific questions with several conditions much better. A section that fully resolves such a question has a real chance there.<\/li>\n<li><strong>Use terms unambiguously:<\/strong> If you name a topic consistently and provide the context, you make it easy for a language model to choose the right meaning.<\/li>\n<\/ol>\n<p>The same rules apply to AI systems that put answers together from web pages. Our <a href=\"https:\/\/taismo.de\/en\/seo-services\/\">SEO services<\/a> describe how we prepare content for both sides, for classic search and for AI answers. The page on <a href=\"https:\/\/taismo.de\/en\/geo\/\">answer engine optimization (AEO, also known as GEO) and AI visibility<\/a> goes deeper into the part aimed specifically at AI systems.<\/p>\n<h2 id=\"faq\">Frequently asked questions about Google BERT<\/h2>\n<p><strong>Is Google BERT a Google update?<\/strong><br \/>\nGoogle announced BERT in October 2019 as a change to Search, which is why people often speak of the BERT update. However, BERT is not a <a href=\"https:\/\/taismo.de\/en\/what-is\/google-core-update\/\">core update<\/a> but a permanently running system for understanding language.<\/p>\n<p><strong>Does BERT also apply to German search queries?<\/strong><br \/>\nYes. In December 2019, Google expanded BERT to more than 70 languages, including German.<\/p>\n<p><strong>What is the difference between BERT and RankBrain?<\/strong><br \/>\nRankBrain relates the words in a search query to concepts and, from 2015 on, helped above all with unfamiliar queries. Since 2019, BERT has understood how the words within a sentence relate to each other. Both systems run in parallel.<\/p>\n<\/p><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>IKI Contents Term profile How does Google BERT work? What did BERT change in Google Search? Does Google still use BERT today? What is the difference between BERT and GPT? Can you optimize for Google BERT? Frequently asked questions about Google BERT What is Google BERT? Google BERT is a language model from Google that [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":8164,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_taismo_jsonld":"","_taismo_h1":"","taismo_jsonld":"{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/taismo.de\/en\/what-is\/google-bert\/\",\"url\":\"https:\/\/taismo.de\/en\/what-is\/google-bert\/\",\"name\":\"What Is Google BERT? 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Both systems run in parallel.\"}}]}]}","footnotes":""},"categories":[80],"tags":[89],"class_list":["post-8168","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-what-is","tag-g-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What Is Google BERT? Language Understanding in Search | taismo<\/title>\n<meta name=\"description\" content=\"Google BERT understands words in the context of the whole sentence. How BERT works, what it changed in Search, and what it means for SEO. 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