This repository contains the source code for the presentation entitled “Persistence as a diagnostic of grammatical status” at DiGS15 by Aaron Ecay and Meredith Tamminga.
In order to replicate the corpus portion of this analysis, you will need a copy of the Penn Parsed Corpus of Middle English (PPCME2) and version 2.003.04 of the CorpusSearch program.
To preform the replication, you should examine the make-corpus.sh
script in the queries
subdirectory of this repository.
-
Edit the line of the file beginning
CS_COMMAND=
so that it points to the location on your computer where the CorpusSearch program is. -
You will also need to place a file named
ppcme2.out
in thequeries
directory that contains the PPCME2 corpus (concatenating together the.psd
files from the corpus release suffices).
Once you have performed these two steps, you should run the
make-corpus.sh
script. The final output of this process is the
coding.cod.ooo
file, a copy of which is also included in this
repository for the convenience of those who do not have access to the
PPCME2. This file is the input to the next stage.
The do-support code relies on the data file do.dat
in the data
subdirectory of this repository. (Please contact the authors if you are
interested in the scripts which generate this file from the PPCEME and
PCEEC). The code for creating the priming graph is contained in the
file dosupp.R
in the scripts
subdirectory.
The analysis is provided in the form of R source code,, in two files in
the scripts
subdirectory. In order to use this code, you will need
to install the stringr
, ggplot2
, plyr
, reshape2
, binom
, and
tikzDevice
packages (the last only if you intend to replicate the
graphs for compilation to PDF, and not merely interactively in the R
console):
install.packages(c("stringr","plyr","ggplot2", "reshape2"))
install.packages(c("tikzDevice"), repos = c("http://R-Forge.R-project.org"))
(The tikzDevice
package has to be installed from a non-default
repository, because it is not distributed on CRAN, the main R package
distribution network.)
You should set R’s working directory to the root of this repository
(e.g. with the setwd()
function.)
Once you have done that, load the two scripts:
source("scripts/data.R")
source("scripts/graphs.R")
Then, load the data into R:
neg <- cleanNegData()
Then, you can make the graphs:
all.graphs()
Inspect the source in the graphs.R
file for more detail about
individual graphs.
In order to recreate the slide show from the LaTeX source, you will need
several LaTeX packages; consult the presentation.tex
and
digs-slides.cls
files for exact details. You should compile the
slides using the lualatex
program, and biber
for the bibliography.
The easiest way to install all the necessary programs is the TeXlive distribution.
If you have comments on the presentation, the analysis, or any aspect of the work, please feel free to email [email protected].