如何识别句子的自定义关键字并在新列中分配自定义名称？

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

# 文本预处理
def preprocess_text(text):
    # 分词
    tokens = word_tokenize(text.lower())
    # 去除停用词和标点符号
    stop_words = set(stopwords.words('english'))
    tokens = [token for token in tokens if token.isalpha() and token not in stop_words]
    return tokens

# 关键字提取
def extract_keywords(text):
    tokens = preprocess_text(text)
    # 使用TF-IDF算法提取关键字
    tfidf = nltk.TfidfVectorizer()
    tfidf.fit_transform([' '.join(tokens)])
    keywords = tfidf.get_feature_names()
    return keywords

# 自定义关键字定义
custom_keywords = ['custom', 'keywords', 'example']

# 关键字匹配和分配自定义名称
def assign_custom_name(text):
    keywords = extract_keywords(text)
    for keyword in keywords:
        if keyword in custom_keywords:
            text = text.replace(keyword, 'CUSTOM_NAME')
    return text

# 示例句子
sentence = "This is an example sentence with custom keywords."

# 分配自定义名称并存储在新列中
new_sentence = assign_custom_name(sentence)
print(new_sentence)