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" ... In this case 812 hours of television news were analyzed by Google’s AI algorithms that identified every character of recognizable text in each 1fps frame, compiling a dataset that could then be subsequently transformed into an ngram histogram, showing what words appeared most often onscreen during this week. ... "
" ... Of course, this represents merely a trivial example of a manual comparison using the ngram dataset. In reality, the real power of such an ngram dataset comes from the ability to perform such comparisons at scale and fully autonomously, cross-correlating the entire dataset against itself to automatically surface such correlations without human intervention or intuition. This makes it possible to identify unexpected correlations and anticorrelations that are where the most interesting findings lie. ... "
" ... What about per-language ngram datasets? Rather than creating ngrams for each homepage over time, what would it look like to aggregate them together by language, creating a linguistic histogram for each of the 110 represented languages, allowing researchers to understand the patterns of language across outlets? ... "