self.ner_per_ents[ent.label_].fp += 1
else:
cand_ents.add((ent.label_, first, last))
current_ent[ent.label_].update([x for x in cand_ents if x[0] == ent.label_])
current_gold[ent.label_].update([x for x in gold_ents if x[0] == ent.label_])
// Scores per ent
[
After Change
cand_deps.add((gold_i, gold_head, token.dep_.lower()))
if "-" not in [token[-1] for token in gold.orig_annot]:
// Find all NER labels in gold and doc
ent_labels = set([x[0] for x in gold_ents]
+ [k.label_ for k in doc.ents])
// Set up all labels for per type scoring and prepare gold per type
gold_per_ents = {ent_label: set() for ent_label in ent_labels}
for ent_label in ent_labels:
if ent_label not in self.ner_per_ents:
self.ner_per_ents[ent_label] = PRFScore()
gold_per_ents[ent_label].update([x for x in gold_ents if x[0] == ent_label])
// Find all candidate labels, for all and per type
cand_ents = set()
cand_per_ents = {ent_label: set() for ent_label in ent_labels}
for ent in doc.ents:
first = gold.cand_to_gold[ent.start]
last = gold.cand_to_gold[ent.end - 1]
if first is None or last is None:
