CODEWITHSUNDEEP
pythonparsingrecursionnltktraversal
EngineeringAsArt
EngineeringAsArt

Reputation: 1382

NLTK: How do I traverse a noun phrase to return list of strings?

In NLTK, how do I traverse a parsed sentence to return a list of noun phrase strings?

I have two goals:
(1) Create the list of Noun Phrases instead of printing them using the 'traverse()' method. I presently use StringIO to record the output of the existing traverse() method. That is not an acceptable solution.
(2) De-parse the Noun Phrase string so: '(NP Michael/NNP Jackson/NNP)' becomes 'Michael Jackson'. Is there a method in NLTK to de-parse?

The NLTK documentation recommends using traverse() to view the Noun Phrase, but how do I capture the 't' in this recursive method so I generate a list of string Noun Phrases?

from nltk.tag import pos_tag

def traverse(t):
  try:
      t.label()
  except AttributeError:
      return
  else:
      if t.label() == 'NP': print(t)  # or do something else
      else:
          for child in t: 
              traverse(child)

def nounPhrase(tagged_sent):
    # Tag sentence for part of speech
    tagged_sent = pos_tag(sentence.split())  # List of tuples with [(Word, PartOfSpeech)]
    # Define several tag patterns
    grammar = r"""
      NP: {<DT|PP\$>?<JJ>*<NN>}   # chunk determiner/possessive, adjectives and noun
      {<NNP>+}                # chunk sequences of proper nouns
      {<NN>+}                 # chunk consecutive nouns
      """
    cp = nltk.RegexpParser(grammar)  # Define Parser
    SentenceTree = cp.parse(tagged_sent)
    NounPhrases = traverse(SentenceTree)   # collect Noun Phrase
    return(NounPhrases)

sentence = "Michael Jackson likes to eat at McDonalds"
tagged_sent = pos_tag(sentence.split())  
NP = nounPhrase(tagged_sent)  
print(NP)

This presently prints:
(NP Michael/NNP Jackson/NNP)
(NP McDonalds/NNP)
and stores 'None' to NP

Upvotes: 8

Views: 3073

Answers (2)

NeuroMorphing
NeuroMorphing

Reputation: 149

An alternative possibility to extract noun phrases is to use the Constituent-Treelib library, which can be installed via: pip install constituent-treelib.

Using this library, we need to perform the following steps to extract the (noun) phrases:

from constituent_treelib import ConstituentTree, BracketedTree

# First, we define the parsed sentence from where we want to extract phrases
parsed_sentence = "(S (NP (NNP Michael) (NNP Jackson)) (VP (VBZ likes) (S (VP (TO to) (VP (VB eat) (PP (IN at) (NP (NNPS McDonalds))))))))"

# ...and wrap the parsed sentence into a BracketedTree object
parsed_sentence = BracketedTree(parsed_sentence)

# Next, we define the language that should be considered with respect to the underlying models 
language = ConstituentTree.Language.English

# You can also specify the desired model for the language ("Small" is selected by default)
spacy_model_size = ConstituentTree.SpacyModelSize.Large

# Now, we create the neccesary NLP pipeline, which is required to create a ConstituentTree object
nlp = ConstituentTree.create_pipeline(language, spacy_model_size) 

# If you wish, you can instruct the library to download and install the models automatically
# nlp = ConstituentTree.create_pipeline(language, spacy_model_size, download_models=True) 

# Now we can instantiate a ConstituentTree object and pass it the parsed sentence as well as the NLP pipeline
tree = ConstituentTree(parsed_sentence, nlp)

# Finally, we can extract all phrases from the tree  
all_phrases = tree.extract_all_phrases(avoid_nested_phrases=True) 

>>> {'S': ['Michael Jackson likes to eat at McDonalds'],
>>> 'NP': ['Michael Jackson'],
>>> 'VP': ['likes to eat at McDonalds'],
>>> 'PP': ['at McDonalds']}

# ...or restrict them only to noun phrases
noun_phrases = all_phrases['NP']

>>> ['Michael Jackson']

In case you also want to visualize the tree, you can do it as follows:

tree.export_tree('my_tree.pdf')

Result:

enter image description here

Upvotes: 0

alvas
alvas

Reputation: 122142

def extract_np(psent):
  for subtree in psent.subtrees():
    if subtree.label() == 'NP':
      yield ' '.join(word for word, tag in subtree.leaves())


cp = nltk.RegexpParser(grammar)
parsed_sent = cp.parse(tagged_sent)
for npstr in extract_np(parsed_sent):
    print (npstr)

Upvotes: 8

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