Python 为什么CoreNLP-ner-tagger和ner-tagger将分开的数字连接在一起?
以下是代码片段:Python 为什么CoreNLP-ner-tagger和ner-tagger将分开的数字连接在一起?,python,nlp,nltk,stanford-nlp,pycorenlp,Python,Nlp,Nltk,Stanford Nlp,Pycorenlp,以下是代码片段: 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')] 我所期望的是: Out[391]: [('
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')]
我所期望的是:
Out[391]:
[('my', 'O'),
('phone', 'O'),
('number', 'O'),
('is', 'O'),
('1111', 'NUMBER'),
('1111', 'NUMBER'),
('1111', 'NUMBER')]
正如您所看到的,人造电话号码由\xa0连接,它被称为不间断空格。我可以通过设置CoreNLP而不更改其他默认规则来区分这一点吗
ner_标记器的定义如下:
ner_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='ner')
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将输入重新排序,并将数字标记与\xa0
(非中断空格)连接起来
长期 让我们浏览一下代码,如果我们查看
corenlparser
中的tag()
函数,我们会看到它调用tag\u sents()
函数,并在调用raw\u tag\u sents()
之前将字符串的输入列表转换为字符串,从而允许corenlparser
重新标记输入,请参阅:
调用时,raw\u tag\u sents()
使用api\u call()
将输入传递给服务器:
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中标记器的选项,就会看到tokenize.whitespace
选项:
api_call()
之前对allow additional属性进行一些更改,我们可以在将令牌传递到由空格连接的CoreNLP服务器时强制执行令牌,例如,对代码的更改:
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')]
您能展示一下如何调用CoreNLPParser
的完整Python代码吗?否则就没有足够的信息来解释为什么会发生这种情况。@alvas是的。我已经更新了。啊,现在这很有趣=)谢谢你抓到这个!在会议上提出的问题
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']]
>>> 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')]