The Google Ngram Viewer or Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of comma-delimited search strings using a yearly count of grams found in sources printed between 1500 and 2008 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, English Fiction, and English One Million; and the 2009 version of most corpora is also available.
The program can search for a single 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 plotted on a graph.
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.
The Ngram Viewer was initially based on the 2009 edition of the Google Books Ngram Corpus. As of January 2016[update], the program can search an individual language's corpus within the 2009 or the 2012 edition.
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.
Google populated the database from over 5 million books published up to 2008. Accordingly, as of January 2016, no data will match beyond the year 2008, no matter if the corpus was generated in 2009 or 2012. 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.
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.
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".
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:
- 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.
As an example, a word "Wikipedia" from the Version 2 file of the English 1-grams is stored as follows:
The graph plotted by the Google Ngram Viewer using the above data is here:
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.
Another issue is that the corpus is in effect a library, containing one of each book. A single, prolific author is thereby able to noticeably insert new phrases into the Google Books lexicon, whether the author is widely read or not.
Guidelines for doing research with data from Google Ngram have been proposed that address many of the issues discussed above.
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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- Google Books Ngram Viewer info page: https://books.google.com/ngrams/info
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- Younes, N., & Reips, U.-D. (2019). Guidelines for improving the reliability of Google Ngram studies: Evidence from religious terms. PLoS One, 14(3): e0213554. https://doi.org/10.1371/journal.pone.0213554
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- Lin, Yuri; et al. (July 2012). "Syntactic Annotations for the Google Books Ngram Corpus" (PDF). Proceedings of the 50th Annual Meeting. Demo Papers. Jeju, Republic of Korea: Association for Computational Linguistics. 2: 169–174. 2390499.
Whitepaper presenting the 2012 edition of the Google Books Ngram Corpus