A python wrapper for Semaphore, a Shallow Semantic Parser that identifies roles in a text.
For any questions, suggestions, or feedback, please e-mail me: jac2130@columbia.edu
#How it can be used:
The detailed description of what Semaphore is and does, i.e, a description of its current scientific uses can be found here: [http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00163]. Semaphore automates Frame-Net style semantic role labeling: [https://framenet.icsi.berkeley.edu/fndrupal/]
Dipanjan Das, Andre Martins, Nathan Schneider, Desai Chen, & Noah A. Smith Language Technologies Institute, Carnegie Mellon University [http://www.ark.cs.cmu.edu/SEMAFOR]
Thus, I will not describe the scientific uses here, but I will simply describe how this program is used from a purely practical perspective as well as the reasons for this Python wrapper.
#Requirements Requires xmltodict to be installed first. This cam be installed with pip using the command:
sudo pip install xmltodict
#To run the program on a list of files. This is recommended because as it stands each time that the program is launced the MST parser is trained anew and that takes a while. This makes running the program on a few sentences not worth the trouble:
from semaphore import semaphore
frames=semaphore(files=file_paths)
where "file_paths" is a python list of file paths in the form of
file_path=['/my_path1/text1.txt', 'my_path2/text2.txt', ...etc.]
The output is a list of python dictionaries, "frames", where frames[i]['text'] is a text string of the i's sentence and where frames[i]['fn-labels'] is a python dictionary of dictionaries (equivalent to a graph), with Frame-Net style frame labels as keys of the outer dictionary, frame element labels as keys of the inner dictionary and raw frame elements which are found in that particular sentence as values of the inner dictionary. An example:
>>> frames[11]['text']
'Air quality issues vary by region .'
>>> frames[11]['fn-labels']
{'Locale': {'Locale': 'region', 'Target': 'region'}, 'Expressing_publicly': {'Target': 'Air'}, 'Point_of_dispute': {'Target': 'issues'}, 'Similarity': {'Entities': 'Air quality issues', 'Target': 'vary'}}
#To run the program on a string:
frames=semaphore("Hello World")
####Note that you can also run the program on both a string and a list of files:
frames = semaphore("Hello World. The following will be from files", files=file_paths)
####If you run semaphore empty, it will run on a supplied example file, like so:
frames =semaphore()
####Seperating running the semaphore program and later loading of the xml files as a list of python dictionaries:
run_semaphore(sample=file_path)
runs semaphore on a single file with path "file_path" and stores the xml file with the frame labels. You can later load in the frames as a python object, provided that you have not run semaphore on other files or strings in the meantime:
frames = import_semaphore()