nltk StanfordNERTagger:如何在没有大写的情况下获得专有名词

我正在尝试使用StanfordNERTagger和nltk从一段文本中提取关键字.

docText="John Donk works for POI. Brian Jones wants to meet with Xyz Corp. for measuring POI's Short Term performance Metrics."

words = re.split("\W+",docText) 

stops = set(stopwords.words("english"))

    #remove stop words from the list
words = [w for w in words if w not in stops and len(w) > 2]

str = " ".join(words)
print str
stn = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz') 
stp = StanfordPOSTagger('english-bidirectional-distsim.tagger') 
stanfordPosTagList=[word for word,pos in stp.tag(str.split()) if pos == 'NNP']

print "Stanford POS Tagged"
print stanfordPosTagList
tagged = stn.tag(stanfordPosTagList)
print tagged

这给了我

John Donk works POI Brian Jones wants meet Xyz Corp measuring POI Short Term performance Metrics
Stanford POS Tagged
[u'John', u'Donk', u'POI', u'Brian', u'Jones', u'Xyz', u'Corp', u'POI', u'Short', u'Term']
[(u'John', u'PERSON'), (u'Donk', u'PERSON'), (u'POI', u'ORGANIZATION'), (u'Brian', u'ORGANIZATION'), (u'Jones', u'ORGANIZATION'), (u'Xyz', u'ORGANIZATION'), (u'Corp', u'ORGANIZATION'), (u'POI', u'O'), (u'Short', u'O'), (u'Term', u'O')]

很明显,像Short和Term被标记为的东西NNP.我拥有的数据包含许多非NNP单词大写的实例.这可能是由于拼写错误或者可能是标题.我对此没有多少控制权.

我如何解析或清理数据,以便我可以检测到非NNP术语,即使它可能是大写的？我不希望术语像Short和Term被归类为NNP

此外,不确定为什么John Donk被捕获为一个人,但Brian Jones不是.可能是由于NNP我的数据中的其他大写的非？这可能对如何StanfordNERTagger对待其他一切产生影响吗？

更新,一种可能的解决方案

这是我打算做的

取每个单词并转换为小写

标记小写字

如果标签是,NNP那么我们知道原始单词也必须是NNP

如果没有,那么原来的单词就是错误的

这是我试图做的

str = " ".join(words)
print str
stp = StanfordPOSTagger('english-bidirectional-distsim.tagger') 
for word in str.split():
    wl = word.lower()
    print wl
    w,pos = stp.tag(wl)
    print pos
    if pos=="NNP":
        print "Got NNP"
        print w

但这给了我错误

John Donk works POI Jones wants meet Xyz Corp measuring POI short term performance metrics
john
Traceback (most recent call last):
  File "X:\crp.py", line 37, in 
    w,pos = stp.tag(wl)
ValueError: too many values to unpack

我尝试了多种方法,但总会出现一些错误.我如何标记单个单词？

我不想将整个字符串转换为小写,然后Tag.如果我这样做,则StanfordPOSTagger返回一个空字符串

首先,看看你的另一个问题,设置斯坦福CoreNLP从命令行或python调用:nltk:如何防止专有名词的阻塞.

对于正确的句子,我们看到NER正常工作:

>>> from corenlp import StanfordCoreNLP
>>> nlp = StanfordCoreNLP('http://localhost:9000')
>>> text = ('John Donk works POI Jones wants meet Xyz Corp measuring POI short term performance metrics. '
... 'john donk works poi jones wants meet xyz corp measuring poi short term performance metrics')
>>> output = nlp.annotate(text, properties={'annotators': 'tokenize,ssplit,pos,ner',  'outputFormat': 'json'})
>>> annotated_sent0 = output['sentences'][0]
>>> annotated_sent1 = output['sentences'][1]
>>> for token in annotated_sent0['tokens']:
...     print token['word'], token['lemma'], token['pos'], token['ner']
... 
John John NNP PERSON
Donk Donk NNP PERSON
works work VBZ O
POI POI NNP ORGANIZATION
Jones Jones NNP ORGANIZATION
wants want VBZ O
meet meet VB O
Xyz Xyz NNP ORGANIZATION
Corp Corp NNP ORGANIZATION
measuring measure VBG O
POI poi NN O
short short JJ O
term term NN O
performance performance NN O
metrics metric NNS O
. . . O

对于降低的句子,你不会得到NNPPOS标签或任何NER标签:

>>> for token in annotated_sent1['tokens']:
...     print token['word'], token['lemma'], token['pos'], token['ner']
... 
john john NN O
donk donk JJ O
works work NNS O
poi poi VBP O
jones jone NNS O
wants want VBZ O
meet meet VB O
xyz xyz NN O
corp corp NN O
measuring measure VBG O
poi poi NN O
short short JJ O
term term NN O
performance performance NN O
metrics metric NNS O

所以你的问题应该是:

您的NLP应用程序的最终目标是什么？

为什么你的输入较低？是你在做什么或如何提供数据？

在回答完这些问题之后,您可以继续决定您对NER标签的真正想法,即

如果输入是低级的,那是因为你构建NLP工具链的方式,那么

不要那样做!!!在普通文本上执行NER而不会出现您创建的扭曲.这是因为NER是在正常文本上训练的,所以它不会真正脱离正常文本的上下文.

另外,尝试不要混合不同套件中的NLP工具,它们通常不会很好用,尤其是在NLP工具链的末尾

如果输入是低位的,因为这是原始数据的方式,那么:

注释一小部分数据,或查找小写的注释数据,然后重新训练模型.

解决它并使用普通文本训练一个真实的文本,然后将truecasing模型应用于较低的文本.请参阅https://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf

如果输入有错误的套管,例如`有些大而有些但不是全部都是正确的名词,那么

尝试真正的解决方案.