gender identification in natural language processing

Question

I have written below code using stanford nlp packages.

GenderAnnotator myGenderAnnotation = new GenderAnnotator();
myGenderAnnotation.annotate(annotation);


        

Answer 1

If your named entity recognizer outputs PERSON for a token, you might use (or build if you don't have one) a gender classifier based on first names. As an example, see the Gender Identification section from the NLTK library tutorial pages. They use the following features:

• Last letter of name.
• First letter of name.
• Length of name (number of characters).
• Character unigram presence (boolean whether a character is in the name).

Though, I have a hunch that using character n-gram frequency---possibly up to character trigrams---will give you pretty good results.

Answer 2

There are a lot of approaches and one of them is outlined in nltk cookbook.

Basically you build a classifier that extract some features (first, last letter, first two, last two letters and so on) from a name and have a prediction based on these features.

import nltk
import random

def extract_features(name):
    name = name.lower()
    return {
        'last_char': name[-1],
        'last_two': name[-2:],
        'last_three': name[-3:],
        'first': name[0],
        'first2': name[:1]
    }

f_names = nltk.corpus.names.words('female.txt')
m_names = nltk.corpus.names.words('male.txt')

all_names = [(i, 'm') for i in m_names] + [(i, 'f') for i in f_names]
random.shuffle(all_names)

test_set = all_names[500:]
train_set= all_names[:500]

test_set_feat = [(extract_features(n), g) for n, g in test_set]
train_set_feat= [(extract_features(n), g) for n, g in train_set]

classifier = nltk.NaiveBayesClassifier.train(train_set_feat)

print nltk.classify.accuracy(classifier, test_set_feat)

This basic test gives you approximately 77% of accuracy.