Q:

Python (NLTK) - more efficient way to extract noun phrases?

I've got a machine learning task involving a large amount of text data. I want to identify, and extract, noun-phrases in the training text so I can use them for feature construction later on in the pipeline. 
I've extracted the type of noun-phrases I wanted from text but I'm fairly new to NLTK, so I approached this problem in a way where I can break down each step in list comprehensions like you can see below. 
But my real question is, am I reinventing the wheel here? Is there a faster way to do this that I'm not seeing?
import nltk
import pandas as pd

myData = pd.read_excel("\User\train_.xlsx")
texts = myData['message']

# Defining a grammar & Parser
NP = "NP: {(<V\w+>|<NN\w?>)+.*<NN\w?>}"
chunkr = nltk.RegexpParser(NP)

tokens = [nltk.word_tokenize(i) for i in texts]

tag_list = [nltk.pos_tag(w) for w in tokens]

phrases = [chunkr.parse(sublist) for sublist in tag_list]

leaves = [[subtree.leaves() for subtree in tree.subtrees(filter = lambda t: t.label == 'NP')] for tree in phrases]

flatten the list of lists of lists of tuples that we've ended up with, into
just a list of lists of tuples
leaves = [tupls for sublists in leaves for tupls in sublists]

Join the extracted terms into one bigram
nounphrases = [unigram[0][1]+' '+unigram[1][0] in leaves]

A:

Take a look at Why is my NLTK function slow when processing the DataFrame?, there's no need to iterate through all rows multiple times if you don't need intermediate steps. 
With ne_chunk and solution from 

NLTK Named Entity recognition to a Python list and
How can I extract GPE(location) using NLTK ne_chunk?

[code]:
from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import RegexpParser
from nltk import Tree
import pandas as pd

def get_continuous_chunks(text, chunk_func=ne_chunk):
    chunked = chunk_func(pos_tag(word_tokenize(text)))
    continuous_chunk = []
    current_chunk = []

    for subtree in chunked:
        if type(subtree) == Tree:
            current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
        elif current_chunk:
            named_entity = " ".join(current_chunk)
            if named_entity not in continuous_chunk:
                continuous_chunk.append(named_entity)
                current_chunk = []
        else:
            continue

    return continuous_chunk

df = pd.DataFrame({'text':['This is a foo, bar sentence with New York city.', 
                           'Another bar foo Washington DC thingy with Bruce Wayne.']})

df['text'].apply(lambda sent: get_continuous_chunks((sent)))

[out]:
0                   [New York]
1    [Washington, Bruce Wayne]
Name: text, dtype: object

To use the custom RegexpParser :
from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import RegexpParser
from nltk import Tree
import pandas as pd

# Defining a grammar & Parser
NP = "NP: {(<V\w+>|<NN\w?>)+.*<NN\w?>}"
chunker = RegexpParser(NP)

def get_continuous_chunks(text, chunk_func=ne_chunk):
    chunked = chunk_func(pos_tag(word_tokenize(text)))
    continuous_chunk = []
    current_chunk = []

    for subtree in chunked:
        if type(subtree) == Tree:
            current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
        elif current_chunk:
            named_entity = " ".join(current_chunk)
            if named_entity not in continuous_chunk:
                continuous_chunk.append(named_entity)
                current_chunk = []
        else:
            continue

    return continuous_chunk

df = pd.DataFrame({'text':['This is a foo, bar sentence with New York city.', 
                           'Another bar foo Washington DC thingy with Bruce Wayne.']})

df['text'].apply(lambda sent: get_continuous_chunks(sent, chunker.parse))

[out]:
0                  [bar sentence, New York city]
1    [bar foo Washington DC thingy, Bruce Wayne]
Name: text, dtype: object

