如何使用Spacy合并相同的连续实体类型

import spacy
from spacy.pipeline import EntityRuler
from spacy.util import filter_spans
ent_list_sample = ["brain", "ischimia", "heart failufe", "parenchyma"]

print("Adding patterns to EntityRuler:n-----------")
patterns = []
for concept in ent_list_sample:
doc = nlp.make_doc(concept)
if len(doc) > 1:
patterns.append({"label": "SCI", "pattern":[{"LOWER":term.text.lower()} for term in doc]})
else:
patterns.append({"label": "SCI", "pattern":doc.text.lower()})
ruler = EntityRuler(nlp)
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)

doc = nlp("It has a brain and also might have brain parenchyma ")
print("Entities:")
print(doc.ents)
output: (brain, brain, parenchyma)
expected: (brain, brain parenchyma)
PS: how we can reach expected output without adding  extra pattern for "brain parenchyma" 

import spacy
from spacy.language import Language
from spacy.tokens import Span
from spacy.pipeline import EntityRuler
from spacy.util import filter_spans
from spacy.pipeline import merge_entities
nlp = spacy.load("en_core_web_sm")
ent_list_sample = ['algorithm', 'data', 'engineering', 'software']
patterns = []
for concept in ent_list_sample:
doc = nlp.make_doc(concept)
if len(doc) > 1:
patterns.append({"label": "SCI", "pattern":[{"LOWER":term.text.lower()} for term in doc]})
else:
patterns.append({"label": "SCI", "pattern":doc.text.lower()})


ent_list_sample1 = ["brain", "ischimia", "heart failufe", "parenchyma"]
patterns1 = []
for concept in ent_list_sample1:
doc = nlp.make_doc(concept)
if len(doc) > 1:
patterns1.append({"label": "HE", "pattern":[{"LOWER":term.text.lower()} for term in doc]})
else:
patterns1.append({"label": "HE", "pattern":doc.text.lower()})
ruler = EntityRuler(nlp)
ruler.add_patterns(patterns+patterns1)
nlp.add_pipe(ruler, before="ner")

class EntityRetokenizeComponent:
def __init__(self, nlp):
pass
def __call__(self, doc):
new_ents = []
for ent in doc.ents:
if ent.label_ == doc[ent.start - 1].ent_type_  and ent.start != 0:
new_ent = Span(doc, ent.start - 1, ent.end, label=ent.label)
new_ents.append(new_ent)
else:
new_ents.append(ent)
doc.ents =filter_spans(new_ents+ list(doc.ents))
return doc


retokenizer = EntityRetokenizeComponent(nlp) 
nlp.add_pipe(retokenizer, name='merge_phrases', last=True)
nlp.add_pipe(merge_entities, last=True)
nlp.pipe_names
doc = nlp("I love Ann is good as well  data software is good for brain parenchyma and Apple is good company")
print([(ent.text, ent.label_) for ent in doc.ents])

这给了我想要的输出：

[("Ann"，"PERSON"(，("数据软件"，"SCI"(