<gh_stars>10-100
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()

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()