Dependency parse large text file with python - parsing

Question

I am trying to parse a large txt file (about 2000 sentence). when I want to set the model_path, I get this massage:
NLTK was unable to find stanford-parser.jar! Set the CLASSPATH
environment variable.
And also when I set the CLASSPATH to this file, another message comes out:
NLTK was unable to find stanford-parser-(\d+)(.(\d+))+-models.jar!
Set the CLASSPATH environment variable.
Would you help me to solve it?
This is my code:
import nltk
from nltk.parse.stanford import StanfordDependencyParser
dependency_parser = StanfordDependencyParser( model_path="edu\stanford\lp\models\lexparser\englishPCFG.ser.gz")
===========================================================================
NLTK was unable to find stanford-parser.jar! Set the CLASSPATH
environment variable.
For more information, on stanford-parser.jar, see:
https://nlp.stanford.edu/software/lex-parser.shtml
import os
os.environ['CLASSPATH'] = "stanford-corenlp-full-2018-10-05/*"
dependency_parser = StanfordDependencyParser( model_path="edu\stanford\lp\models\lexparser\englishPCFG.ser.gz")
===========================================================================
NLTK was unable to find stanford-parser.jar! Set the CLASSPATH
environment variable.
For more information, on stanford-parser.jar, see:
https://nlp.stanford.edu/software/lex-parser.shtml
os.environ['CLASSPATH'] = "stanford-corenlp-full-2018-10-05/stanford-parser-full-2018-10-17/stanford-parser.jar"
>>> dependency_parser = StanfordDependencyParser( model_path="stanford-corenlp-full-2018-10-05/stanford-parser-full-2018-10-17/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
NLTK was unable to find stanford-parser-(\d+)(.(\d+))+-models.jar!
Set the CLASSPATH environment variable.
For more information, on stanford-parser-(\d+)(.(\d+))+-models.jar, see:
https://nlp.stanford.edu/software/lex-parser.shtml

Answer 1

You should get the new stanfordnlp dependency parser that is native to Python!
It will run slower on the CPU than GPU, but it still should run reasonably fast.
Just run pip install stanfordnlp to install.
import stanfordnlp
stanfordnlp.download('en') # This downloads the English models for the neural pipeline
nlp = stanfordnlp.Pipeline() # This sets up a default neural pipeline in English
doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.")
doc.sentences[0].print_dependencies()
There is also a helpful command line tool:
python -m stanfordnlp.run_pipeline -l en example.txt
Full details here: https://stanfordnlp.github.io/stanfordnlp/
GitHub: https://github.com/stanfordnlp/stanfordnlp