2

这是代码片段： 

In [390]: t
Out[390]: ['my', 'phone', 'number', 'is', '1111', '1111', '1111']

In [391]: ner_tagger.tag(t)
Out[391]: 
[('my', 'O'),
 ('phone', 'O'),
 ('number', 'O'),
 ('is', 'O'),
 ('1111\xa01111\xa01111', 'NUMBER')]

我期望的是：

Out[391]: 
[('my', 'O'),
 ('phone', 'O'),
 ('number', 'O'),
 ('is', 'O'),
 ('1111', 'NUMBER'),
 ('1111', 'NUMBER'),
 ('1111', 'NUMBER')]

如您所见，人造电话号码由 \xa0 连接，据说这是一个不间断的空格。我可以通过在不更改其他默认规则的情况下设置 CoreNLP 来将其分开吗？

ner_tagger 定义为：

ner_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='ner')
4

1 回答 1

1

TL;博士

NLTK 将令牌列表读入字符串，然后将其传递给 CoreNLP 服务器。CoreNLP 重新标记输入并将类似数字的标记与\xa0（不间断空格）连接起来。

在长

让我们看一下代码，如果我们tag()从 中查看函数CoreNLPParser，我们会看到它调用tag_sents()函数并将输入的字符串列表转换为字符串，然后再调用raw_tag_sents()允许CoreNLPParser重新标记输入的方法，请参见https://github。 com/nltk/nltk/blob/develop/nltk/parse/corenlp.py#L348：

def tag_sents(self, sentences):
    """
    Tag multiple sentences.
    Takes multiple sentences as a list where each sentence is a list of
    tokens.

    :param sentences: Input sentences to tag
    :type sentences: list(list(str))
    :rtype: list(list(tuple(str, str))
    """
    # Converting list(list(str)) -> list(str)
    sentences = (' '.join(words) for words in sentences)
    return [sentences[0] for sentences in self.raw_tag_sents(sentences)]

def tag(self, sentence):
    """
    Tag a list of tokens.
    :rtype: list(tuple(str, str))
    >>> parser = CoreNLPParser(url='http://localhost:9000', tagtype='ner')
    >>> tokens = 'Rami Eid is studying at Stony Brook University in NY'.split()
    >>> parser.tag(tokens)
    [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'), ('at', 'O'), ('Stony', 'ORGANIZATION'),
    ('Brook', 'ORGANIZATION'), ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'O')]
    >>> parser = CoreNLPParser(url='http://localhost:9000', tagtype='pos')
    >>> tokens = "What is the airspeed of an unladen swallow ?".split()
    >>> parser.tag(tokens)
    [('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'),
    ('airspeed', 'NN'), ('of', 'IN'), ('an', 'DT'),
    ('unladen', 'JJ'), ('swallow', 'VB'), ('?', '.')]
    """
    return self.tag_sents([sentence])[0]

调用时，raw_tag_sents()使用以下命令将输入​​传递给服务器api_call()：

def raw_tag_sents(self, sentences):
    """
    Tag multiple sentences.
    Takes multiple sentences as a list where each sentence is a string.

    :param sentences: Input sentences to tag
    :type sentences: list(str)
    :rtype: list(list(list(tuple(str, str)))
    """
    default_properties = {'ssplit.isOneSentence': 'true',
                          'annotators': 'tokenize,ssplit,' }

    # Supports only 'pos' or 'ner' tags.
    assert self.tagtype in ['pos', 'ner']
    default_properties['annotators'] += self.tagtype
    for sentence in sentences:
        tagged_data = self.api_call(sentence, properties=default_properties)
        yield [[(token['word'], token[self.tagtype]) for token in tagged_sentence['tokens']]
                for tagged_sentence in tagged_data['sentences']]

所以问题是如何解决问题并在传递令牌时获取令牌？

如果我们查看 CoreNLP 中 Tokenizer 的选项，我们会看到以下tokenize.whitespace选项：

properties如果我们在调用之前对 allow additional 进行一些更改api_call()，我们可以在令牌传递到由空格连接的 CoreNLP 服务器时强制执行令牌，例如更改代码：

def tag_sents(self, sentences, properties=None):
    """
    Tag multiple sentences.

    Takes multiple sentences as a list where each sentence is a list of
    tokens.

    :param sentences: Input sentences to tag
    :type sentences: list(list(str))
    :rtype: list(list(tuple(str, str))
    """
    # Converting list(list(str)) -> list(str)
    sentences = (' '.join(words) for words in sentences)
    if properties == None:
        properties = {'tokenize.whitespace':'true'}
    return [sentences[0] for sentences in self.raw_tag_sents(sentences, properties)]

def tag(self, sentence, properties=None):
    """
    Tag a list of tokens.

    :rtype: list(tuple(str, str))

    >>> parser = CoreNLPParser(url='http://localhost:9000', tagtype='ner')
    >>> tokens = 'Rami Eid is studying at Stony Brook University in NY'.split()
    >>> parser.tag(tokens)
    [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'), ('at', 'O'), ('Stony', 'ORGANIZATION'),
    ('Brook', 'ORGANIZATION'), ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'O')]

    >>> parser = CoreNLPParser(url='http://localhost:9000', tagtype='pos')
    >>> tokens = "What is the airspeed of an unladen swallow ?".split()
    >>> parser.tag(tokens)
    [('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'),
    ('airspeed', 'NN'), ('of', 'IN'), ('an', 'DT'),
    ('unladen', 'JJ'), ('swallow', 'VB'), ('?', '.')]
    """
    return self.tag_sents([sentence], properties)[0]

def raw_tag_sents(self, sentences, properties=None):
    """
    Tag multiple sentences.

    Takes multiple sentences as a list where each sentence is a string.

    :param sentences: Input sentences to tag
    :type sentences: list(str)
    :rtype: list(list(list(tuple(str, str)))
    """
    default_properties = {'ssplit.isOneSentence': 'true',
                          'annotators': 'tokenize,ssplit,' }

    default_properties.update(properties or {})

    # Supports only 'pos' or 'ner' tags.
    assert self.tagtype in ['pos', 'ner']
    default_properties['annotators'] += self.tagtype
    for sentence in sentences:
        tagged_data = self.api_call(sentence, properties=default_properties)
        yield [[(token['word'], token[self.tagtype]) for token in tagged_sentence['tokens']]
                for tagged_sentence in tagged_data['sentences']]

更改上述代码后：

>>> from nltk.parse.corenlp import CoreNLPParser
>>> ner_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='ner')
>>> sent = ['my', 'phone', 'number', 'is', '1111', '1111', '1111']
>>> ner_tagger.tag(sent)
[('my', 'O'), ('phone', 'O'), ('number', 'O'), ('is', 'O'), ('1111', 'DATE'), ('1111', 'DATE'), ('1111', 'DATE')]
于 2018-09-10T07:22:41.320 回答