Nltk: Tag CoreNLPParser () deve permitir sobrecarga de propriedades

Com o CoreNLPParser.tag() atual, a "retocagem" do Stanford CoreNLP é inesperada:

>>> 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\xa01111\xa01111', 'NUMBER')]

O comportamento esperado deve ser:

>>> 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')]

A solução proposta é permitir a sobrecarga de properties argumentos para .tag() e .tag_sents() , ou seja, em https://github.com/nltk/nltk/blob/develop/nltk/parse/ corenlp.py # L348 e por padrão use properties = {'tokenize.whitespace':'true'} porque estamos concatenando os tokens por espaços em tag_sents() .

    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']]

Isso deve reforçar a lista de tokens de string inseridos pelos usuários.

Detalhes em https://stackoverflow.com/questions/52250268/why-do-corenlp-ner-tagger-and-ner-tagger-join-the-separated-numbers-together

Se permitirmos que .tag() sobrecarregue as propriedades antes de raw_tag_sents , isso também permitirá aos usuários lidar facilmente com casos como # 1876