Python 如何在stanford nlp中查找命名实体的索引

我正在为斯坦福nlp使用python包装器 查找命名实体的代码为：

sentence = "Mr. Jhon was noted to have a cyst at his visit back in 2011."
result = nlp.ner(sentence)

for ne in result:
  if ne[1] == 'PERSON':
     print(ne)

输出为列表类型的结果： （u'Jhon'，u'PERSON'）

但它没有像spaCy或其他nlp工具那样给出命名实体的索引，而是使用索引给出结果

>> namefinder = NameFinder.getNameFinder("spaCy")
>> entities = namefinder.find(sentences)
List(List((PERSON,0,13), (DURATION,15,27), (DATE,76,83)),
  List((PERSON,4,10),  (LOCATION,77,86), (ORGANIZATION,26,39)),
  List((PERSON,0,13), (DURATION,16,28), (ORGANIZATION,52,80)))

---

我正在为此使用

nltk

。我将答案改编自。关键点是调用use和方法

span\u tokenize（）

生成一个单独的列表，我称之为

span

，该列表保留每个令牌的范围

from nltk.tag import StanfordNERTagger
from nltk.tokenize import WordPunctTokenizer

# Initialize Stanford NLP with the path to the model and the NER .jar
st = StanfordNERTagger(r"C:\stanford-corenlp\stanford-ner\classifiers\english.all.3class.distsim.crf.ser.gz",
       r"C:\stanford-corenlp\stanford-ner\stanford-ner.jar",
       encoding='utf-8')

sentence = "Mr. Jhon was noted to have a cyst at his visit back in 2011."

tokens = WordPunctTokenizer().tokenize(sentence)

# We have to compute the token spans in a separate list
# Notice that span_tokenize(sentence) returns a generator 
spans = list(WordPunctTokenizer().span_tokenize(sentence))

# enumerate will help us keep track of the token index in the token lists 
for i, ner in enumerate(st.tag(tokens)):
    if ner[1] == "PERSON":
        print spans[i], ner