The Google n-gram project is an example of how one might extract useful information by indexing large numbers of documents. You might, for example, look at tre

The Google n-gram project is an example of how one might extract useful information by indexing large numbers of documents. You might, for example, look at trends in the use of a given scientific instrument, or with a bit of NP, when the field switched from using "computer" to mean a machine instead of a person. It was an eye-opener to see how gendered the scientific papers were in the 1960s, where the scientist is invariably a "he" and the secretary/typist is a "she". Some simple questions I have are: how have those trends changed over time? Is it different for different subfields? It's impossible to do that on my own when the papers cost even $1 per copy.