如何使文本匹配不出现误报？

def context_aware_match(text, keyword):
    if keyword in text:
        # 简单示例：检查关键词前后的词是否合理
        words = text.split()
        index = words.index(keyword)
        if index > 0 and index < len(words) - 1:
            prev_word = words[index - 1]
            next_word = words[index + 1]
            if is_relevant_context(prev_word, next_word):
                return True
    return False

def is_relevant_context(prev_word, next_word):
    # 这里可以定义更复杂的逻辑来判断上下文是否相关
    return True  # 示例中简单返回True

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def nlp_based_match(texts, query):
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(texts + [query])
    similarity_scores = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1]).flatten()
    return similarity_scores > THRESHOLD  # THRESHOLD是预设的相似度阈值