The spike centers on 1869, and there's another spike in 1897 and 1900. OCR wasn't as good as it is today. becomes the bigram they 're, we'll becomes we Developed and maintained by the Python community, for the Python community. tags, _ROOT_ doesn't stand for a particular word or position Google Ngram Viewer. Jessica Kormos is a writer and editor with 15 years' experience writing articles, copy, and UX content for Tecca.com, Rosenfeld Media, and many others. States, what percentage of them are "nursery school" or "child care"? Books searches. Because there weren't a lot of books published during that time and because the data is set to smooth, the picture is distorted. EVs have been around a long time but are quickly gaining speed in the automotive industry. analyzing the syntax; you can think of it as a placeholder for what Assessing the accuracy of these predictions is You're searching in an unexpected corpus. 1800 - 1992 1993 1994 - 2004 English (2009) About Ngram Viewer . year, which means that all of the scanned books from early years are Google Books Ngram Viewer. var data = [{"ngram": "drink=>*_NOUN", "parent": "", "type": "NGRAM_COLLECTION", "timeseries": [2.380641490162816e-06, 2.4192295370539792e-06, 2.3543674127305767e-06, 2.3030458160227293e-06, 2.232196671059228e-06, 2.1610477146184948e-06, 2.1364835660619974e-06, 2.066405615762181e-06, 1.944526272065364e-06, 1.8987424539318452e-06, 1.8510785519002382e-06, 1.793903669928503e-06, 1.7279300844766763e-06, 1.6456588493188712e-06, 1.6015212643034308e-06, 1.5469109411826918e-06, 1.5017512597280207e-06, 1.473403072184608e-06, 1.4423894500380032e-06, 1.4506490718499012e-06, 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