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xrenner

eXternally configurable REference and Non Named Entity Recognizer

https://corpling.uis.georgetown.edu/xrenner/

Usage:

xrenner.py [options] INFILE (> OUTFILE)
xrenner.py [options] *.conllx

Options:

-m, --model input model name in models/, default 'eng'
-o, --output output format, default: sgml; alternatives: html, paula, webanno, conll, onto, unittest
-x, --override specify a section model's override.ini file with alternative settings; e.g. OntoNotes or GUM for English
-v, --verbose output run time and summary
-r, --rulebased
 rule based operation, disable stochastic classifiers in selected model
-d, --dump <FILE>
 dump all anaphor-antecedent candidate pairs to <FILE> to train classifiers
-p, --procs NUM
 number of processes to run in parallel (only useful if running on multiple documents)
-t, --test run unit tests and quit
--version print xrenner version and quit

Input format:

1       Wikinews        Wikinews        NP      NNP     _       2       nsubj   _       _
2       interviews      interview       VVZ     VBZ     _       0       root    _       _
3       President       president       NN      NN      _       2       dobj    _       _
4       of      of      IN      IN      _       3       prep    _       _
5       the     the     DT      DT      _       7       det     _       _
6       International   international   NP      NNP     _       7       amod    _       _
7       Brotherhood     brotherhood     NP      NNP     _       4       pobj    _       _
8       of      of      IN      IN      _       7       prep    _       _
9       Magicians       magician        NPS     NNPS    _       8       pobj    _       _

1       Wednesday       Wednesday       NP      NNP     _       0       root    _       _
2       ,       ,       ,       ,       _       0       punct   _       _
3       October October NP      NNP     _       4       nn      _       _
4       9       9       CD      CD      _       1       appos   _       _
5       ,       ,       ,       ,       _       0       punct   _       _
6       2013    2013    CD      CD      _       4       tmod    _       _

Installation:

Download the repo and use the main xrenner.py script on an input file, or install from PyPI and import as a module:

> pip install xrenner

Examples:

  • python xrenner.py example_in.conll10 > example_out.sgml
  • python xrenner.py -x GUM example_in.conll10 > example_out.sgml
  • python xrenner.py -o conll example_in.conll10 > example_out.conll
  • python xrenner.py -m eng -o conll *.conll10 (automatically names output files based on input files)

Note that by default, the English model is invoked (-m eng), and this model expects input in Basic Stanford Typed Dependencies (not Universal Dependencies).

Module usage:

from xrenner import Xrenner

xrenner = Xrenner()
# Get a parse in basic Stanford Dependencies (not UD)
my_conllx_result = some_parser.parse("John visited Spain. His visit went well.")

sgml_result = xrenner.analyze(my_conllx_result,"sgml")
print(sgml_result)