如何在 stanford nlp 中找到命名实体的索引答案

【问题标题】：How to find the index of named entity in stanford nlp如何在 stanford nlp 中找到命名实体的索引
【发布时间】：2017-08-24 09:41:46
【问题描述】：

我正在为 stanford nlp 使用 python 包装器查找命名实体的代码是：

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)

输出是列表类型的结果： (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，它给出了带有索引的结果。

>> 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)))

【问题讨论】：

标签： python stanford-nlp named-entity-recognition

【解决方案1】：

我为此使用nltk。我改编了here 的答案。关键是调用 use WordPunctTokenizer 和方法 span_tokenize() 来生成一个单独的列表，我称之为 spans ，它保持每个令牌的跨度。

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

【讨论】：