how to get parse tree using python nltk?

Older question, but you can use nltk together with the bllipparser. Here is a longer example from nltk. After some fiddling I myself used the following:

To install (with nltk already installed):

sudo python3 -m nltk.downloader bllip_wsj_no_aux
pip3 install bllipparser

To use:

from nltk.data import find
from bllipparser import RerankingParser

model_dir = find('models/bllip_wsj_no_aux').path
parser = RerankingParser.from_unified_model_dir(model_dir)

best = parser.parse("The old oak tree from India fell down.")

print(best.get_reranker_best())
print(best.get_parser_best())

Output:

-80.435259246021 -23.831876011253 (S1 (S (NP (NP (DT The) (JJ old) (NN oak) (NN tree)) (PP (IN from) (NP (NNP India)))) (VP (VBD fell) (PRT (RP down))) (. .)))
-79.703612178593 -24.505514522222 (S1 (S (NP (NP (DT The) (JJ old) (NN oak) (NN tree)) (PP (IN from) (NP (NNP India)))) (VP (VBD fell) (ADVP (RB down))) (. .)))

Here is alternative solution using StanfordCoreNLP instead of nltk. There are few library that build on top of StanfordCoreNLP, I personally use pycorenlp to parse the sentence.

First you have to download stanford-corenlp-full folder where you have *.jar file inside. And run the server inside the folder (default port is 9000).

export CLASSPATH="`find . -name '*.jar'`"
java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer [port?] # run server

Then in Python, you can run the following in order to tag the sentence.

from pycorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP('http://localhost:9000')

text = "The old oak tree from India fell down."

output = nlp.annotate(text, properties={
  'annotators': 'parse',
  'outputFormat': 'json'
})

print(output['sentences'][0]['parse']) # tagged output sentence

To get parse tree using nltk library you can use the following code

# Import required libraries
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
from nltk import pos_tag, word_tokenize, RegexpParser

# Example text
sample_text = "The quick brown fox jumps over the lazy dog"

# Find all parts of speech in above sentence
tagged = pos_tag(word_tokenize(sample_text))

#Extract all parts of speech from any text
chunker = RegexpParser("""
                    NP: {<DT>?<JJ>*<NN>} #To extract Noun Phrases
                    P: {<IN>}            #To extract Prepositions
                    V: {<V.*>}           #To extract Verbs
                    PP: {<p> <NP>}       #To extract Prepositional Phrases
                    VP: {<V> <NP|PP>*}   #To extract Verb Phrases
                    """)

# Print all parts of speech in above sentence
output = chunker.parse(tagged)
print("After Extracting\n", output)

# output looks something like this
 (S
  (NP The/DT old/JJ oak/NN)
  (NP tree/NN)
  (P from/IN)
  India/NNP
  (VP (V fell/VBD))
  down/RB
  ./.)

You can also get a graph for this tree

# To draw the parse tree
output.draw()

Output graph looks like this