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

Question

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]

Answer 1

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

Answer 2

I suggest referring to this prior thread: Extracting all Nouns from a text file using nltk

They suggest using TextBlob as the easiest way to achieve this (if not the one that is most efficient in terms of processing) and the discussion there addresses your question.

from textblob import TextBlob
txt = """Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages."""
blob = TextBlob(txt)
print(blob.noun_phrases)