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Using TextGrams.jl

TextGrams.jl is a library that we use to score the relatedness of 2 or more books based on their ngrams (i.e. phrases made up of “N” words in a row, where a useful value of N is 1 to about 4 or so).

Here is a rundown of its use, from downloading books from archive.org to creating a baseline, and then comparing books.


You should have julia installed, and optionally ruby for the following downloading books step.

Downloading 19th Century Books

We have a convenient Ruby library that takes care of this part—see archdown. If you have a working Ruby environment on your system, you should be able to install the gem and immediately begin fetching books:

$ gem install archdown
$ archdown -y 1750-1850

It will create a ‘library’ folder with many 2-letter folders inside, and another set of 2-letter folders inside those. Each book will be downloaded to the folder corresponding to the first 2 and last 2 letters of its archive.org id. For instance, latewarbetween_00hunt will download to ./library/la/nt/. The reason we do this is there are some archive.org id prefixes (e.g. jstor) that have an abundant set of files associated with them, so we need a second level of folders.

Note: Downloading all books from 1750 to 1850 will probably take several days and 200 GB or so of space.

Creating a Baseline

The first thing to do once you have a lot of books is to create an ngram baseline. This is where we measure the “normal” frequency of each ngram in the English language during the period (i.e. the 19th century in our example).

It’s important to create a baseline so that, later, when we find matches between books (during the comparison step below) we can weight the matches by importance—the more rare the ngrams are in English, the more “significant” the match.

Let’s pick 100 books at random:

find . -name '*.txt' | shuf -n 100 > baseline-files.txt

(Note: shuf is part of gnu core utils. On ubuntu, you can install it with apt-get install coreutils; and on Mac OS, brew install coreutils, but it will be called gshuf instead.)

Now, let’s create a baseline with N set to 3 (i.e. this will include phrases of one, two, and three words):

julia baseline.jl -n 3 `cat baseline-files.txt` > baseline.txt

Great! Now we have a baseline to feed the compare.jl script.

Comparing Books for Matching Ngrams

Let’s say we have a large text, book1.txt and we want to compare it with another, book2.txt. Here’s how:

julia compare.jl -n 3 -b baseline.txt book1.txt X book2.txt

Using the baseline we calculated above, we’ll get some raw scores that might look something like this:


We’ll talk more about what these results mean below.

The X between book1.txt and book2.txt in the command above is significant. This is a special marker that indicates all files before the X are to be matched against all files after the X. In our simple example, we’re just comparing one book against another, but using this X as a separator, it is also easy to compare all books in, say, “set A” to all books in “set B”. It might look like this:

julia compare.jl -n 3 -b baseline.txt book1.txt book2.txt X bookA.txt bookB.txt

Which would compare all pairs, book1 x bookA, book1 x bookB, book2 x bookA, and book2 x bookB.

Let’s say we want a little more detail—like the actual ngrams that matched between the two books, for instance. We can use the -s switch which is short for --show-matches. In addition, we can pair it with -t to set the threshold score for word matches. In this case, let’s make the minimum word match score 0.01:

julia compare.jl -n 3 -b baseline.txt -v -s -t 0.01 book1.txt X book2.txt

Now we get a lot more verbose output:

Using baseline: baseline.txt
Using files X: {"book1.txt"}
Using files Y: {"book2.txt"}
Measuring baseline size...
  87097 ngrams
elapsed time: 0.383290306 seconds (40527888 bytes allocated, 8.20% gc time)

Cross comparing...
Loading book1.txt...
Loading book2.txt...
Comparing... book1.txt x book2.txt

And there are individual ngrams listed with scores next to them:

community situation0.2500.5000.354book1book2
health positions0.1250.1250.125book1book2
experienced nurse0.0830.1670.118book1book2

Reading the Results

Let’s look again at the “total” scores we received as a summary of the strength of relatedness between book1 and book2:

  • total_inv: sum total of the inverse baseline frequency of all matching ngrams.
  • total_mul: like total_inv, but whereas the numerator in total_env is always 1.0, the numerator in total_mul is the product of the count of occurrences of the ngram in document X, and the count of occurrences of the ngram in document Y.
  • total_sqrt: like total_mul, but the numerator is the square root of the product, instead of just the product.
  • sizex: the total count of words in document X.
  • sizey: the total count of words in document Y.
  • namex: the name of document X (filename).
  • namey: the name of document Y (filename).

Why break it down in this way? i.e. why not a single number representing the score? We’re still experimenting with the best formula to represent the relatedness between books. To normalize the score, the length of the books must be taken in to account somehow. We provide several “totals” so that we can experiment with different length-normalized scores. For instance, some possibilities:

total_inv / (sizex + sizey)

total_sqrt / sqrt(sizex**2 + sizey**2)

If you copy the output of compare.jl to a spreadsheet, each of these normalized scores and others can be easily experimented with.

We’ve found that books with a wordcount smaller than 20k are not reliably scored using the ngram matching method. Because natural language is a constantly evolving target, people independently generate some language phrases that are, by random chance, the same as what others generate. Therefore, not all matches can be indicators of a certain relationship between books.

Published 29 Mar 2015

The WordTree Foundation studies the relationships between books, with a special interest in LDS scripture such as the Book of Mormon.
WordTree Foundation on Github