There are a lot of logical ways to extend out Bookworm to work with established tools for text analysis. We want to make this easy without requiring enormous bloat in the core package.
The /extensions folder in a Bookworm is not created by default, but can be filled with folders; each folder is an installed extension.
Each extension should be a git repository that can be cloned into that extensions location. The single command make run in that directory should run the build; it's permissible for an extension to require command-line input.
That directory location means it will have access to your local bookworm configuration and other information. For example: once the geotagger builds its own metadata file,
bookworm add_metadata extensions/geotagger/metadata.txt filename
The topic modeling extension adjusts the master wordcounts file so that each word has a topic assignment.
It's hard to quantify the error associated with counts over time. Bookworm-bootstrap builds a bunch of different bootstrapped samples in which the same book can appear multiple times; that gives some sense of what random variation might be doing to your sample.
The bootstrapper lets you replicate sample error by drawing from your full sample: the bookworm-samples package simply creates a new dummy metadata variable for every single document in your set, assigning it to one of 100 groups.
This makes it possible to draw from a random distribution to see if the results you're getting are realistic.
Given the name of field that contains place names, add in a longitude-latitude formatted set of places.
Previously we stored latitude and longitude as separate fields. While there were a few nice features to that (you could search inside a bounding box), it was pretty inefficient compared to grouping on a single point. So now Bookworm stores all geolocation data as json strings consisting of latitude,longitude. So the location of New York City would be encoded as: "[40.7127,-74.0059]".
You could probably also write that as "[40.7127,285.9941]"; but as a standard, we'll say that the negative numbers are the only supported one. (Unless someone tells me otherwise is better).
The Geotagger will work by parsing a text using the Stanford NLTK named entity extractor, pulling out persons, locations, and entities. The results could then be run through the geopositioner plugin.
This would be the most amazing thing possible, and possibly worth building in as core funcationality. In essence: say you have a field author_id, and that corresponds to an identifier on some other page. By using a linked open data interchange, you could simply specify the source and the middle element of an RDF triple, and have the values automatically pulled in by some SPARQL process or something. The use of specific URIs in the field_descriptions.json field would enormously facilitate this.
Implement the Ngrams serial killer algorithm in Bookworm. Been done once, but might be a nice pocket example of how this can work on the OL/Hathi data.
On my mind because of one Bookworm in particular, but with an academic audience it might be nice to have some pathway to populating texts that contain unstructured text of university names with all the data about universities in ICOADS.
Given a field that contains names (first or complete), add fields to the Bookworm that include gender both as a flat determination and as a probability. Ideally, should take some logic about the birth year of the person into account (19th century Leslies are male, etc.). Could be implemented using my old code, which is a bit more flexible, or Lincoln Mullen's R package, which I've used on one Bookworm.
Extensions may access not just the database, but the raw creation files themselves.
Currently, a few extensions access the input.txt file. For example, in the geotagger extension:
metadata.txt:
cat ../../files/texts/input.txt | parallel --block-size 1M --pipe ./tagAChunk.sh > metadata.txt
This is clearly a bad idea, since it will break on other file storage techniques. Newer extension should use the protocol bookworm tokenize text_stream, or if you want whitespace-delimited tokens, bookworm tokenize text_stream | book tokenize token_stream.