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nGrams are words or phrases, tokenized and counted in a defined corpus and displayed as a graph showing how relative frequencies of those phrases have occurred as occurring over publication date. The two tools referenced below provide a basis for exploration of ngrams.  Each tool is bound to secondary data derived from analysis of a different corpus, so results of the same nGram will not necessarily align. 

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• How easily can these examples above be refined and improved?
• Compare the two interfaces, especially as to the affordances and the limits of each. 
• What additional elements of control would be useful that aren't available?
• When we see unexpected wave forms, what do we make of these? 
  
  • Do these constitute discoveries or represent errors?  How can we distinguish?
  • Would the flaws be due to the data, the metadata, the algorithms?

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• Compare one collection of books to a second collection, and report the differences in frequency of tokens - ShksprDunning
• Extract entities from a set of books, and list out referents of where they occur
  
  • Person, location - WSPlaysEntityExtract
  • Dates - WSComediesExtractDates (Note the wide variety of what is considered a "date")
  • Dates visualized over timeline - WSComediesDateExtractSimileRemix
• Model "topics", or clusters of tokens that are statistically more likely to be found together - WSComediesTopics; WSTragediesTopics

Discussion

• In general, do the results look valid? Do any of these algorithms yield results that might be considered a false positive?

• Note that results can be downloadedNote the portability of the results.  Can you think of advantages to this portability? 
• Are there things that the researcher would want or need to know about these algorithms when making claims about results?

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