Extracting noun phrases from NLTK using python

Question

I am new to both python and nltk. I have converted the code from https://gist.github.com/alexbowe/879414 to the below given code to make it run for many documents/text chunks. But I got the following error

 Traceback (most recent call last):
 File "E:/NLP/PythonProgrames/NPExtractor/AdvanceMain.py", line 16, in    <module>
  result = np_extractor.extract()
 File "E:\NLP\PythonProgrames\NPExtractor\NPExtractorAdvanced.py", line 67,   in extract
 for term in terms:
File "E:\NLP\PythonProgrames\NPExtractor\NPExtractorAdvanced.py", line 60, in get_terms
 for leaf in self.leaves(tree):
 TypeError: leaves() takes 1 positional argument but 2 were given

Can any one help me to fix this problem. I have to extract noun phrases from millions of product reviews. I used Standford NLP kit using Java, but it was extremely slow, so I thought using nltk in python will be better. Please also recommend if there is any better solution.

import nltk
from nltk.corpus import stopwords
stopwords = stopwords.words('english')
grammar = r"""
 NBAR:
    {<NN.*|JJ>*<NN.*>}  # Nouns and Adjectives, terminated with Nouns
 NP:
    {<NBAR>}
    {<NBAR><IN><NBAR>}  # Above, connected with in/of/etc...
"""
   lemmatizer = nltk.WordNetLemmatizer()
   stemmer = nltk.stem.porter.PorterStemmer()

class NounPhraseExtractor(object):

    def __init__(self, sentence):
        self.sentence = sentence

    def execute(self):
        # Taken from Su Nam Kim Paper...
        chunker = nltk.RegexpParser(grammar)
        #toks = nltk.regexp_tokenize(text, sentence_re)
        # #postoks = nltk.tag.pos_tag(toks)
        toks = nltk.word_tokenize(self.sentence)
        postoks = nltk.tag.pos_tag(toks)
        tree = chunker.parse(postoks)
        return tree

    def leaves(tree):
        """Finds NP (nounphrase) leaf nodes of a chunk tree."""
        for subtree in tree.subtrees(filter=lambda t: t.label() == 'NP'):
            yield subtree.leaves()

    def normalise(word):
        """Normalises words to lowercase and stems and lemmatizes it."""
        word = word.lower()
        word = stemmer.stem_word(word)
        word = lemmatizer.lemmatize(word)
        return word

    def acceptable_word(word):
        """Checks conditions for acceptable word: length, stopword."""
        accepted = bool(2 <= len(word) <= 40
                    and word.lower() not in stopwords)
        return accepted

    def get_terms(self,tree):
        for leaf in self.leaves(tree):
            term = [self.normalise(w) for w, t in leaf if self.acceptable_word(w)]
        yield term

    def extract(self):
        terms = self.get_terms(self.execute())
        matches = []
        for term in terms:
            for word in term:
                matches.append(word)
        return matches

Answer 1

You need to either:

• decorate each of normalize, acceptable_word, and leaves with @staticmethod, or
• add a self parameter as the first parameter of these methods.

You're calling self.leaves which will pass self as an implicit first parameter to the leaves method (but your method only takes a single parameter). Making these static methods, or adding a self parameter will fix this issue.

(your later calls to self.acceptable_word,and self.normalize will have the same issue)

You could read about Python's static methods in their docs, or possibly from an external site that may be easier to digest.