This is a Python binding to the Natural Language Processing suite Frog. Frog is intended for Dutch and performs part-of-speech tagging, lemmatisation, morphological analysis, named entity recognition, shallow parsing, and dependency parsing. The tool itseelf is implemented in C++ (http://ilk.uvt.nl/frog).
For easy installation, please use our LaMachine distribution
- Make sure to first install Frog and all its dependencies
- Install Cython if not yet available on your system:
$ sudo apt-get cython cython3(Debian/Ubuntu, may differ for others) - Run:
$ sudo python setup.py install
Example:
from __future__ import print_function, unicode_literals #to make this work on Python 2 as well as Python 3
import frog
frog = frog.Frog(frog.FrogOptions(parser=False))
output = frog.process_raw("Dit is een test")
print("RAW OUTPUT=",output)
output = frog.process("Dit is nog een test.")
print("PARSED OUTPUT=",output)Output:
RAW OUTPUT= 1 Dit dit [dit] VNW(aanw,pron,stan,vol,3o,ev)
0.777085 O B-NP
2 is zijn [zijn] WW(pv,tgw,ev) 0.999891 O
B-VP
3 een een [een] LID(onbep,stan,agr) 0.999113 O
B-NP
4 test test [test] N(soort,ev,basis,zijd,stan) 0.789112
O I-NP
PARSED OUTPUT= [{'chunker': 'B-NP', 'index': '1', 'lemma': 'dit', 'ner':
'O', 'pos': 'VNW(aanw,pron,stan,vol,3o,ev)', 'posprob': 0.777085, 'text':
'Dit', 'morph': '[dit]'}, {'chunker': 'B-VP', 'index': '2', 'lemma':
'zijn', 'ner': 'O', 'pos': 'WW(pv,tgw,ev)', 'posprob': 0.999966, 'text':
'is', 'morph': '[zijn]'}, {'chunker': 'B-NP', 'index': '3', 'lemma': 'nog',
'ner': 'O', 'pos': 'BW()', 'posprob': 0.99982, 'text': 'nog', 'morph':
'[nog]'}, {'chunker': 'I-NP', 'index': '4', 'lemma': 'een', 'ner': 'O',
'pos': 'LID(onbep,stan,agr)', 'posprob': 0.995781, 'text': 'een', 'morph':
'[een]'}, {'chunker': 'I-NP', 'index': '5', 'lemma': 'test', 'ner': 'O',
'pos': 'N(soort,ev,basis,zijd,stan)', 'posprob': 0.903055, 'text': 'test',
'morph': '[test]'}, {'chunker': 'O', 'index': '6', 'eos': True, 'lemma':
'.', 'ner': 'O', 'pos': 'LET()', 'posprob': 1.0, 'text': '.', 'morph':
'[.]'}]
Available keyword arguments for FrogOptions:
- tok - True/False - Do tokenisation? (default: True)
- lemma - True/False - Do lemmatisation? (default: True)
- morph - True/False - Do morpholigical analysis? (default: True)
- daringmorph - True/False - Do morphological analysis in new experimental style? (default: False)
- mwu - True/False - Do Multi Word Unit detection? (default: True)
- chunking - True/False - Do Chunking/Shallow parsing? (default: True)
- ner - True/False - Do Named Entity Recognition? (default: True)
- parser - True/False - Do Dependency Parsing? (default: False).
- xmlin - True/False - Input is FoLiA XML (default: False)
- xmlout - True/False - Output is FoLiA XML (default: False)
- docid - str - Document ID (for FoLiA)
- numThreads - int - Number of threads to use (default: unset, unlimited)
Frog supports output in the FoLiA XML format (set FrogOptions(xmlout=True)), as
well as FoLiA input (set FrogOptions(xmlin=True)). The FoLiA format exposes more details about the linguistic
annotation in a more structured and more formal way.
Whenever FoLiA output is requested, the process() method will return an instance of folia.Document, which is
provided by the FoLiApy library. This loads the entire FoLiA document in memory and
allows you to inspect it in any way you see fit. Extensive documentation for this library can be found here:
http://folia.readthedocs.io/
An example can be found below:
from frog import Frog, FrogOptions
frog = Frog(FrogOptions(parser=True,xmlout=True))
output = frog.process("Dit is een FoLiA test.")
#output is now no longer a string but an instance of folia.Document, provided by the FoLiA library in PyNLPl (pynlpl.formats.folia)
print("FOLIA OUTPUT AS RAW XML=")
print(output.xmlstring())
print("Inspecting FoLiA output (just a small example):")
for word in output.words():
print(word.text() + " " + word.pos() + " " + word.lemma())