使用NLTK实现对象标准化

import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk

# 下载必要的资源
nltk.download('punkt')
nltk.download('maxent_ne_chunker')
nltk.download('words')
nltk.download('averaged_perceptron_tagger')

# 示例文本
text = "Barack Obama was born in Hawaii. He was the 44th President of the United States."

# 分词
tokens = word_tokenize(text)

# 词性标注
tagged = pos_tag(tokens)

# 命名实体识别
named_entities = ne_chunk(tagged)

# 打印命名实体
print(named_entities)

# 进一步处理命名实体（例如，链接到知识库）
def normalize_entity(entity):
    if hasattr(entity, 'label') and entity.label() == 'PERSON':
        return "Barack Obama"
    elif hasattr(entity, 'label') and entity.label() == 'GPE':
        return "Hawaii"
    return str(entity)

normalized_entities = [normalize_entity(subtree) for subtree in named_entities if hasattr(subtree, 'label')]
print(normalized_entities)