nltk.pos_tag函数

nltk.pos_tag()函数是一种用来进行词性标注的工具。

def pos_tag(tokens, tagset=None, lang='eng'):
    """
    Use NLTK's currently recommended part of speech tagger to
    tag the given list of tokens.

        >>> from nltk.tag import pos_tag
        >>> from nltk.tokenize import word_tokenize
        >>> pos_tag(word_tokenize("John's big idea isn't all that bad."))
        [('John', 'NNP'), ("'s", 'POS'), ('big', 'JJ'), ('idea', 'NN'), ('is', 'VBZ'),
        ("n't", 'RB'), ('all', 'PDT'), ('that', 'DT'), ('bad', 'JJ'), ('.', '.')]
        >>> pos_tag(word_tokenize("John's big idea isn't all that bad."), tagset='universal')
        [('John', 'NOUN'), ("'s", 'PRT'), ('big', 'ADJ'), ('idea', 'NOUN'), ('is', 'VERB'),
        ("n't", 'ADV'), ('all', 'DET'), ('that', 'DET'), ('bad', 'ADJ'), ('.', '.')]

    NB. Use `pos_tag_sents()` for efficient tagging of more than one sentence.

    :param tokens: Sequence of tokens to be tagged
    :type tokens: list(str)
    :param tagset: the tagset to be used, e.g. universal, wsj, brown
    :type tagset: str
    :param lang: the ISO 639 code of the language, e.g. 'eng' for English, 'rus' for Russian
    :type lang: str
    :return: The tagged tokens
    :rtype: list(tuple(str, str))
    """
    tagger = _get_tagger(lang)
    return _pos_tag(tokens, tagset, tagger)

从其源码中我们可以发现它默认支持英文
另外，我对语法标注的过程函数 _get_tagger的源码也进行了了解

def _get_tagger(lang=None):
    if lang == 'rus':
        tagger = PerceptronTagger(False)
        ap_russian_model_loc = 'file:' + str(find(RUS_PICKLE))
        tagger.load(ap_russian_model_loc)
    else:
        tagger = PerceptronTagger（)
    return tagger

可以发现其核心实现在于 PerceptronTagger（）class PerceptronTagger(TaggerI):

 Greedy Averaged Perceptron tagger, as implemented by Matthew Honnibal.
    See more implementation details here:
        http://spacy.io/blog/part-of-speech-POS-tagger-in-python/

    >>> from nltk.tag.perceptron import PerceptronTagger

    Train the model 

    >>> tagger = PerceptronTagger(load=False)

    >>> tagger.train([[('today','NN'),('is','VBZ'),('good','JJ'),('day','NN')],
    ... [('yes','NNS'),('it','PRP'),('beautiful','JJ')]])

    >>> tagger.tag(['today','is','a','beautiful','day'])
    [('today', 'NN'), ('is', 'PRP'), ('a', 'PRP'), ('beautiful', 'JJ'), ('day', 'NN')]

    Use the pretrain model (the default constructor) 

    >>> pretrain = PerceptronTagger()

    >>> pretrain.tag('The quick brown fox jumps over the lazy dog'.split())
    [('The', 'DT'), ('quick', 'JJ'), ('brown', 'NN'), ('fox', 'NN'), ('jumps', 'VBZ'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dog', 'NN')]

    >>> pretrain.tag("The red cat".split())
    [('The', 'DT'), ('red', 'JJ'), ('cat', 'NN')]

从上述代码中, 可以得出在PerceptronTagger()函数没有参数的时候，使用的是已经训练好的词性标注工具。

初次学习，后续还会慢慢摸索