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33

Google Ngrams has problems for ELU questions: it is not speech, which is often the unspoken context of questions, even though we only research things through print. it is not the web, which has more colloquial usage (again more often the question context here) it includes a lot of strange publications it has a lot of OCR errors for older books (with messy ...


15

This was originally intended as an answer to the practical question of how current use of a term could best be gauged. As such, the answer lists common resources available for that purpose. Google Ngrams/Google Books do not satisfy the demand: being raw statistical data (the n-grams from the Google Books corpus) based on raw textual data (Google Books), ...


10

re AmE vs BrE: Dickens is published in the US and UK. Most popular authors will be in both. Check out the ngrams info for the source of the publications. re speech vs print: ngrams only captures those things that have been printed in books, not transcribed speech. So lots of spoken slang, nuances if pronunciation, regional varieties are sparsely ...


6

Since the header asks broadly "How reliable is Ngram?" I thought it might be useful to point out a significant aspect of using Ngram as a search tool that casual users may not be aware of: the results change within the same period of years depending on what date range you set. Ngram's default range is 1800–2000, but the tool permits you to set the endpoints ...


6

The fact is, NGrams can be a good tool for examining the language in published books. In fact, OED uses it as part of calculating word frequency! However, the results may not be accurate unless you go and verify they are. OCR The fact is, Google Books relies a lot on OCR. Unfortunately, there are a lot more OCR errors than just long s (which also confuses ...


4

Some of the BYU corpora are web corpora. And the best part is that it's freee! iWeb: The Intelligent Web-based Corpus "14 billion words from the Web" News on the Web (NOW) "8.7 billion, Web news, 2010-last month" Global Web-Based English (GloWbE) "1.9 billion, Web, 20 countries" Wikipedia Corpus "1.9 billion, Wikipedia" Corpus of Online Registers of ...


3

The tire / tyre example would be heavily confounded by tire as in become tired, and to a tiny extent by places in Lebanon and New York (wp), as well as the iron tyres mentioned in the comments. So that serves as an example of how ngrams can easily fool or be fooled.


2

If you use Ngram and just look at the graph you are looking at garbage. If you carefully examine a representative number of the actual references you get a much better idea as to whether the graph is meaningful or not. Once you become a billionaire you may certainly create your own database (being sure to observe copyright limitations!) and set up your own ...


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