eXternally configurable REference and Non Named Entity Recognizer
https://corpling.uis.georgetown.edu/xrenner/
Usage:
xrenner.py [options] INFILE (> OUTFILE) xrenner.py [options] *.conllu
Options:
| -m, --model | input model name in models/, default 'eng' |
| -o, --output | output format, default: sgml; alternatives: html, paula, webanno, webannotsv, 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 |
More exotic options:
| --oracle | use external file with entity type predictions per token span (for integrating separate NER) |
| --noseq | do not use machine learning sequence tagger even when available |
Input format:
1 Wikinews _ PROPN NNP _ 2 nsubj _ _ 2 interviews _ VERB VBZ _ 0 root _ _ 3 President _ NOUN NN _ 2 obj _ _ 4 of _ ADP IN _ 7 case _ _ 5 the _ DET DT _ 7 det _ _ 6 International _ PROPN NNP _ 7 amod _ _ 7 Brotherhood _ PROPN NNP _ 3 nmod _ _ 8 of _ ADP IN _ 9 case _ _ 9 Magicians _ PROPN NNPS _ 7 nmod _ _ 1 Wednesday _ PROPN NNP _ 0 root _ _ 2 , _ PUNCT , _ 4 punct _ _ 3 October _ PROPN NNP _ 4 compound _ _ 4 9 _ NUM CD _ 1 appos _ _ 5 , _ PUNCT , _ 6 punct _ _ 6 2013 _ NUM CD _ 4 nmod:tmod _ _
Format for external NER predictions when using --oracle option:
Soaking the Bowl in Boiling Water 2,4 object|5,7 substance 2,4 object|5,7 substance Choose artwork with cool colors . 2,6 object|4,6 abstract 2,3 object|4,6 abstract
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
- 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 Universal Dependencies.
To use neural entity classification and machine learning coreference prediction with the English model, flair and xgboost must be installed (see requirements.txt)
from xrenner import Xrenner
xrenner = Xrenner()
# Get a parse in Universal Dependencies
my_conllx_result = some_parser.parse("John visited Spain. His visit went well.")
sgml_result = xrenner.analyze(my_conllx_result,"sgml")
print(sgml_result)