A lightweight program that duplicates the basic functionality of the "mark" function in the prodigy system using the same input and output formats; output is saved to a file which is coder- and time-stamped rather than to a database.
To run program:
python3 plovigy-mark.py <filename> <coder>
<filename> is the file to annotate;
<coder> is typically the coder initials ("PLV" is the default). The file plovigy_testfile_0105.jsonl is sample input.
python3 plovigy-mark.py verify_test0105_02.jsonl PAS
Output file will have a name of the format
|space||ignore (useful if entering ' ', a, x from lower left of keyboard)|
|`||ignore (useful if entering `, 1, 2 from top row of keyboard)|
|0||ignore (useful if entering 0, 1, 2 from numerical keypad)|
|return||accept, reject, or ignore depending on setting|
|c||add "comment" to "meta" in output record: program prompts for text|
|d||toggle between accept/reject/ignore (initial value is "accept")|
|m||toggle display of the "meta" information|
|-||go back one record in buffer|
|+||go forward one record in buffer without recoding|
At present, key commands except for the default
return must be followed by
Some usage notes
returnoption substantially increases the speed at which one can annotate in situations where one hits a sequence of cases which are either usually right, usually wrong or usually irrelevant.
Input is not case-sensitive.
Cases are saved in a temporary buffer before being written: this currently holds 8 entries (probably beyond the capacity of your working memory) but can be changed using the global BUFFER_SIZE. This can be navigated using the +/- keys; to change the annonated "answer" value use the accept/reject/ignore options.
In the normal forward flow, a comment ('C' key) needs to be added before adding the annotation since after an annotation is added, the program moves to the next record. Comments can be added by going backwards in the buffer.
The file FILEREC_NAME -- currently set to "plovigy.filerecs.txt" -- keeps track of the location in the file, so if you quit and restart, you will be returned to the last uncoded record in the file.
The file plover_reference.html is a reference to the PLOVER ontology and has some suggestions for doing annotation; it can be opened in a browser or from this link.
plovigy_testfile_0105.jsonl is set up for annotating the primary PLOVER event but, with different data preparation, this could be -- and in the future will be -- changed so that the program can be used for the annotation of the source or target actors, or the PLOVER mode or context of the event. We have a set of programs which extract the most frequently-used patterns for the PETRARCH-1 and PETRARCH-2 programs (see this presentation) and then convert these to the
prodigy/plovigyformat, but at the moment these are fairly ad hoc: still, if you might find them useful, contact firstname.lastname@example.org.
What's the point?
This program was developed to do simple annotation -- that is, simply determining whether a coding was correct, incorrect, or the text should not have been coded -- on the records coded into the PLOVER system (hence the name) by several different automated event data coding programs, simply presenting the text and the assigned category without any additional markup. While
prodigy is way cool, it involves considerable overhead, and we are looking for something that could run, say, on a cheap little Ubuntu burner laptop on, say, a trans-Atlantic flight. As it happens, the simplicity of the interface also means that one can classify cases very quickly.
There are obviously work-arounds -- see StackOverflow -- that would allow the program to respond immediately to any key, without the
return, though so far I haven't found this sufficiently irritating to bother including that feature: in fact having to use two keys before doing anything except annotating with the default seems useful.
I'm guessing in the appropriate Python framework, it would be easy to get this running on a smart phone: again, it's really lightweight. This is left as an exercise.
This program was developed as part of research funded by a U.S. National Science Foundation "Resource Implementations for Data Intensive Research in the Social Behavioral and Economic Sciences (RIDIR)" project: Modernizing Political Event Data for Big Data Social Science Research (Award 1539302; PI: Patrick Brandt, University of Texas at Dallas)
This program has been successfully run under Mac OS 10.13.2 and Ubuntu 16.04; it is standard Python 3.5 and so also should run in Windows. Not that I can see any reason you'd want to run Windows.
This code is covered under the MIT license: http://opensource.org/licenses/MIT
Report bugs to: email@example.com