使用当前的CoreNLPParser.tag()
,斯坦福 CoreNLP 的“重新标记化”是出乎意料的:
>>> 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')]
预期的行为应该是:
>>> 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')]
建议的解决方案是允许properties
.tag()
和.tag_sents()
properties
参数重载,即https://github.com/nltk/nltk/blob/develop/nltk/parse/ corenlp.py#L348并且默认情况下使用properties = {'tokenize.whitespace':'true'}
因为我们在tag_sents()
通过空格连接令牌。
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']]
这应该强制用户输入的字符串令牌列表。
如果我们允许.tag()
在raw_tag_sents
之前重载属性,这也将允许用户轻松处理像 #1876 这样的情况
看起来挺好的。
只是一些小意见。 它应该是if properties is None
,而不是if properties == None
。 assert self.tagtype in ['pos', 'ner']
应该是assert self.tagtype in ['pos', 'ner'], "CoreNLP tagger supports only 'pos' or 'ner' tags."
。
我真的不喜欢连接和拆分字符串的想法,也许有一种方法可以将单词列表作为句子而不是简单的字符串传递给 CoreNLP。
你好,我想把这个作为我的第一个问题。
很高兴你对这个问题感兴趣。 如果您有任何问题,请在此处提问。
最有用的评论
看起来挺好的。
只是一些小意见。 它应该是
if properties is None
,而不是if properties == None
。assert self.tagtype in ['pos', 'ner']
应该是assert self.tagtype in ['pos', 'ner'], "CoreNLP tagger supports only 'pos' or 'ner' tags."
。我真的不喜欢连接和拆分字符串的想法,也许有一种方法可以将单词列表作为句子而不是简单的字符串传递给 CoreNLP。