POSTagger.py

import nltk
nltk.download('averaged_perceptron_tagger')


class POSTagger(object):
    def __init__(self):
        pass

    def pos_tag(self, sentences):
        """
        input format: list of lists of words
            e.g.: [['this', 'is', 'a', 'sentence'], ['this', 'is', 'another', 'one']]
        output format: list of lists of tagged tokens. Each tagged tokens has a
        form, a lemma, and a list of tags
            e.g: [[('this', 'this', ['DT']), ('is', 'be', ['VB']), ('a', 'a', ['DT']), ('sentence', 'sentence', ['NN'])],
                    [('this', 'this', ['DT']), ('is', 'be', ['VB']), ('another', 'another', ['DT']), ('one', 'one', ['CARD'])]]
        """

        pos = [nltk.pos_tag(sentence) for sentence in sentences]
        # adapt format
        pos = [[(word, word, [postag]) for (word, postag) in sentence] for sentence in pos]
        return pos