为什么CoreNLP ner和ner要将分离的数字连接在一起？
为什么CoreNLP ner和ner要将分离的数字连接在一起？
提问于 2018-09-10 02:19:35
下面是代码片段：
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，人工电话号码由\xa0连接，这据说是一个不破坏的空间。我可以通过在不改变其他默认规则的情况下设置CoreNLP来区分这一点。
ner_tagger被定义为：
ner_tagger = CoreNLPParser(url='http://localhost:9000', tagtype='ner')回答 1
Stack Overflow用户
回答已采纳
发布于 2018-09-10 07:22:41
TL;DR
NLTK正在将令牌列表读取到字符串中，然后将其传递给CoreNLP服务器。CoreNLP对输入进行重新标记，并将类似数字的令牌与\xa0 (非破缺空间)连接起来。
长时间
让我们看一看代码，如果我们从tag()中看到CoreNLPParser函数，我们会看到它调用了tag_sents()函数，并在调用允许CoreNLPParser重新标记输入的raw_tag_sents()之前将输入字符串列表转换为字符串，请参阅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api_call()将输入传递给服务器。
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，并在传入令牌时获得令牌？
如果我们查看CoreNLP中托卡器的选项，就会看到tokenize.whitespace选项：
- https://stanfordnlp.github.io/CoreNLP/tokenize.html#options
- Preventing tokens from containing a space in Stanford CoreNLP
如果在调用properties之前对api_call()进行了一些更改，则可以在将令牌传递给由空格连接的CoreNLP服务器时强制执行，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')]页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/52250268
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