def resolve_pronouns(doc):
coref = Coref()
coref.one_shot_coref(utterances=doc.text)
mentions = coref.get_mentions()
#print(mentions, coref.get_scores())
clusters = coref.get_clusters(remove_singletons=True)
alias_groups = []
for cluster in clusters[0].values():
# cluster here is a list of mention indices
aliases = []
indices = []
for mention_index in cluster:
mention = mentions[mention_index]
aliases.append(mention.text)
indices.append((mention.start, mention.end))
alias_groups.append((aliases, indices))
return alias_groups
from neuralcoref import Coref coref = Coref() clusters = coref.continuous_coref(utterances=u"John wanted to marry Mary. He was a doctor and she was a nurse. She went to the market one day when Henry met her and proposed her. He was a local don.") print(clusters) mentions = coref.get_mentions() #print(mentions) utterances = coref.get_utterances() print(utterances) resolved_utterance_text = coref.get_resolved_utterances() print(resolved_utterance_text) print(coref.get_scores())
from nltk.tokenize import word_tokenize
import sys
import os
sys.stdout = open(os.devnull, "w")
resolved_sentence = []
con = sys.argv[1].replace("_", " ")
utt = sys.argv[2].replace("_", " ")
coref = Coref()
clusters = coref.one_shot_coref(utterances=unicode(utt), context=unicode(con))
candidates = coref.get_mentions()
utterances = coref.get_utterances()
resolved_utterance_text = coref.get_resolved_utterances()
sys.stdout = sys.__stdout__
First = str(candidates[0]).split()
one = []
for item in candidates:
one.append(str(item))
two = []
Three = []
for item in one: