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
You need to either:
normalize
, acceptable_word
, and leaves
with @staticmethod, or 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.
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