Coreference resolution in python nltk using Stanford coreNLP

Question

Stanford CoreNLP provides coreference resolution as mentioned here, also this thread, this, provides some insights about its implementation in Java.

However, I am

Answer 1

stanfordcorenlp, the relatively new wrapper, may work for you.

Suppose the text is "Barack Obama was born in Hawaii. He is the president. Obama was elected in 2008."

The code:

# coding=utf-8

import json
from stanfordcorenlp import StanfordCoreNLP

nlp = StanfordCoreNLP(r'G:\JavaLibraries\stanford-corenlp-full-2017-06-09', quiet=False)
props = {'annotators': 'coref', 'pipelineLanguage': 'en'}

text = 'Barack Obama was born in Hawaii.  He is the president. Obama was elected in 2008.'
result = json.loads(nlp.annotate(text, properties=props))

num, mentions = result['corefs'].items()[0]
for mention in mentions:
    print(mention)

Every "mention" above is a Python dict like this:

{
  "id": 0,
  "text": "Barack Obama",
  "type": "PROPER",
  "number": "SINGULAR",
  "gender": "MALE",
  "animacy": "ANIMATE",
  "startIndex": 1,
  "endIndex": 3,
  "headIndex": 2,
  "sentNum": 1,
  "position": [
    1,
    1
  ],
  "isRepresentativeMention": true
}