Python 自定义命名实体识别

我有这样一句话:

text="The weather is extremely severe in England"

我想做一个定制的Name Entity Recognition (NER)度手术

首先，正常的NER过程将输出带有GPE标签的England

pip install spacy

!python -m spacy download en_core_web_lg

import spacy
nlp = spacy.load('en_core_web_lg')

doc = nlp(text)

for ent in doc.ents:
    print(ent.text+' - '+ent.label_+' - '+str(spacy.explain(ent.label_)))

Result: England - GPE - Countries, cities, states

然而，我希望整句话都有标签High-Severity.

因此，我正在执行以下步骤:

from spacy.strings import StringStore

new_hash = StringStore([u'High_Severity']) # <-- match id
nlp.vocab.strings.add('High_Severity')

from spacy.tokens import Span

# Get the hash value of the ORG entity label
High_Severity = doc.vocab.strings[u'High_Severity']  

# Create a Span for the new entity
new_ent = Span(doc, 0, 7, label=High_Severity)

# Add the entity to the existing Doc object
doc.ents = list(doc.ents) + [new_ent]

我接受以下错误:

ValueError: [E1010] Unable to set entity information for token 6 which is included in more than one span in entities, blocked, missing or outside.

据我所知，这是因为NER已经将England识别为GRE，不能在现有标签上添加标签.

我试图执行定制的NER代码(也就是，没有先运行正常的NER代码)，但这并没有解决我的问题.

对如何解决这个问题有什么 idea 吗？