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The Google Ngram Viewer or Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n-grams found in sources printed between 1500 and 2019 in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish. There are also some specialized English corpora, such as American English, British English, and English Fiction.
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The program can search for a word or a phrase, including misspellings or gibberish. The n-grams are matched with the text within the selected corpus, optionally using case-sensitive spelling (which compares the exact use of uppercase letters), and, if found in 40 or more books, are then displayed as a graph.
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The Google Ngram Viewer supports searches for parts of speech and wildcards. It is routinely used in research.
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The program was developed by Jon Orwant and Will Brockman and released in mid-December 2010. It was inspired by a prototype called "Bookworm" created by Jean-Baptiste Michel and Erez Aiden from Harvard's Cultural Observatory and Yuan Shen from MIT and Steven Pinker.
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The Ngram Viewer was initially based on the 2009 edition of the Google Books Ngram Corpus. As of July 2020, the program supports 2009, 2012, and 2019 corpora.
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Operation and restrictions
Commas delimit user-entered search-terms, indicating each separate word or phrase to find. The Ngram Viewer returns a plotted line chart within seconds of the user pressing the Enter key or the "Search" button on the screen.
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As an adjustment for more books having been published during some years, the data are normalized, as a relative level, by the number of books published in each year.
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Due to limitations on the size of the Ngram database, only matches found in at least 40 books are indexed in the database; otherwise the database could not have stored all possible combinations.
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Typically, search terms cannot end with punctuation, although a separate full stop (a period) can be searched. Also, an ending question mark (as in "Why?") will cause a second search for the question mark separately.
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Omitting the periods in abbreviations will allow a form of matching, such as using "R M S" to search for "R.M.S." versus "RMS".
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Corpora
The corpora used for the search are composed of total_counts, 1-grams, 2-grams, 3-grams, 4-grams, and 5-grams files for each language. The file format of each of the files is tab-separated data. Each line has the following format:
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total_counts file
year TAB match_count TAB page_count TAB volume_count NEWLINE
Version 1 ngram file (generated in July 2009)
ngram TAB year TAB match_count TAB page_count TAB volume_count NEWLINE
Version 2 ngram file (generated in July 2012)
ngram TAB year TAB match_count TAB volume_count NEWLINE
The Google Ngram Viewer uses match_count to plot the graph.
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As an example, a word "Wikipedia" from the Version 2 file of the English 1-grams is stored as follows:
ngram year match_count volume_count
Wikipedia 1904 1 1
Wikipedia 1912 11 1
Wikipedia 1924 1 1
Wikipedia 1925 11 1
Wikipedia 1929 11 1
Wikipedia 1943 11 1
Wikipedia 1946 11 1
Wikipedia 1947 11 1
Wikipedia 1949 11 1
Wikipedia 1951 11 1
Wikipedia 1953 22 2
Wikipedia 1955 11 1
Wikipedia 1958 1 1
Wikipedia 1961 22 2
Wikipedia 1964 22 2
Wikipedia 1965 11 1
Wikipedia 1966 15 2
Wikipedia 1969 33 3
Wikipedia 1970 129 4
Wikipedia 1971 44 4
Wikipedia 1972 22 2
Wikipedia 1973 1 1
Wikipedia 1974 2 1
Wikipedia 1975 33 3
Wikipedia 1976 11 1
Wikipedia 1977 13 3
Wikipedia 1978 11 1
Wikipedia 1979 112 12
Wikipedia 1980 13 4
Wikipedia 1982 11 1
Wikipedia 1983 3 2
Wikipedia 1984 48 3
Wikipedia 1985 37 3
Wikipedia 1986 6 4
Wikipedia 1987 13 2
Wikipedia 1988 14 3
Wikipedia 1990 12 2
Wikipedia 1991 8 5
Wikipedia 1992 1 1
Wikipedia 1993 1 1
Wikipedia 1994 23 3
Wikipedia 1995 4 1
Wikipedia 1996 23 3
Wikipedia 1997 6 1
Wikipedia 1998 32 10
Wikipedia 1999 39 11
Wikipedia 2000 43 12
Wikipedia 2001 59 14
Wikipedia 2002 105 19
Wikipedia 2003 149 53
Wikipedia 2004 803 285
Wikipedia 2005 2964 911
Wikipedia 2006 9818 2655
Wikipedia 2007 20017 5400
Wikipedia 2008 33722 6825
The graph plotted by the Google Ngram Viewer using the above data is here:
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Criticism
The data set has been criticized for its reliance upon inaccurate OCR, an overabundance of scientific literature, and for including large numbers of incorrectly dated and categorized texts. Because of these errors, and because it is uncontrolled for bias (such as the increasing amount of scientific literature, which causes other terms to appear to decline in popularity), it is risky to use this corpus to study language or test theories. Since the data set does not include metadata, it may not reflect general linguistic or cultural change and can only hint at such an effect.
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Guidelines for doing research with data from Google Ngram have been proposed that address many of the issues discussed above.
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OCR issues
Optical character recognition, or OCR, is not always reliable, and some characters may not be scanned correctly. In particular, systemic errors like the confusion of "s" and "f" in pre-19th century texts (due to the use of the long s which was similar in appearance to "f") can cause systemic bias. Although Google Ngram Viewer claims that the results are reliable from 1800 onwards, poor OCR and insufficient data mean that frequencies given for languages such as Chinese may only be accurate from 1970 onward, with earlier parts of the corpus showing no results at all for common terms, and data for some years containing more than 50% noise.
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REF:
https://en.wikipedia.org/wiki/Google_Ngram_Viewer
https://ai.googleblog.com/2006/08/all-our-n-gram-are-belong-to-you.html
