我是 NER 的新手，我一直在尝试使用 Spacy 提取名称。这是我的代码：

import spacy
spacy_nlp = spacy.load('en_core_web_sm')

doc = spacy_nlp(text.strip())

# create sets to hold words 
named_entities = set()
money_entities = set()
organization_entities = set()
location_entities = set()
time_indicator_entities = set()

for i in doc.ents:
    entry = str(i.lemma_).lower()
    text = text.replace(str(i).lower(), "")
    # Time indicator entities detection
    if i.label_ in ["TIM", "DATE"]:
        time_indicator_entities.add(entry)
    # money value entities detection
    elif i.label_ in ["MONEY"]:
        money_entities.add(entry)
    # organization entities detection
    elif i.label_ in ["ORG"]:
        organization_entities.add(entry)
    # Geographical and Geographical entities detection
    elif i.label_ in ["GPE", "GEO"]:
        location_entities.add(entry)
    # extract artifacts, events and natural phenomenon from text
    elif i.label_ in ["ART", "EVE", "NAT", "PERSON"]:
        named_entities.add(entry.title())

该模型似乎对某些类型的名称具有不错的准确性。然而，它不知道人们的名字在世界各地是如何不同的（不适应文化差异）。是否有可能的解决方法来避免这种偏见？