import nltk
from nltk.chunk import tree2conlltags
from nltk.corpus import names
import random
class AnaphoraExample:
def __init__(self):
males = [(name, 'male') for name in names.words('male.txt')]
females = [(name, 'female') for name in names.words('female.txt')]
combined = males + females
random.shuffle(combined)
training = [(self.feature(name), gender) for (name, gender) in combined]
self._classifier = nltk.NaiveBayesClassifier.train(training)
def feature(self, word):
return {'last(1)' : word[-1]}
def gender(self, word):
return self._classifier.classify(self.feature(word))
def learnAnaphora(self):
sentences = [
"John is a man. He walks",
"John and Mary are married. They have two kids",
"In order for Ravi to be successful, he should follow John",
"John met Mary in Barista. She asked him to order a Pizza"
]
for sent in sentences:
chunks = nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent)), binary=False)
stack = []
print(sent)
items = tree2conlltags(chunks)
for item in items:
if item[1] == 'NNP' and (item[2] == 'B-PERSON' or item[2] == 'O'):
stack.append((item[0], self.gender(item[0])))
elif item[1] == 'CC':
stack.append(item[0])
elif item[1] == 'PRP':
stack.append(item[0])
print("\t {}".format(stack))
anaphora = AnaphoraExample()
anaphora.learnAnaphora()
