Google N-Gram

A Google’s machine learning application to plot the graph of word frequency over time. The N-Gram is useful to identify the historical trends of the use of specific words over time. The user could access to the specific years to identify the listed books by clicking the specific points in the graph to be linked with the related books snips. Sources:

• Younes, N., & Reips, U. D. (2019). Guideline for improving the reliability of Google Ngram studies: Evidence from religious terms. PloS one, 14(3), e0213554.
• Cavnar, W. B., & Trenkle, J. M. (1994, April). N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval (Vol. 161175).
• Jean-Baptiste Michel*, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, William Brockman, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden*. Quantitative Analysis of Culture Using Millions of Digitized Books. Science (Published online ahead of print: 12/16/2010)
• Yuri Lin, Jean-Baptiste Michel, Erez Lieberman Aiden, Jon Orwant, William Brockman, Slav Petrov. Syntactic Annotations for the Google Books Ngram Corpus. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics Volume 2: Demo Papers (ACL '12) (2012)