[自然语言处理|NLP]NLP在更深层次的语义理解的应用：从原理到实践

# 示例代码：NLP在文本情感分析中的应用
import openai

openai.api_key = "YOUR_API_KEY"

def analyze_sentiment(text):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"分析文本情感：{text}\n",
        max_tokens=150
    )

    return response.choices[0].text.strip()

# 示例
text_to_analyze = "这部电影太令人兴奋了！"
sentiment_result = analyze_sentiment(text_to_analyze)

print("情感分析结果:", sentiment_result)

# 示例代码：NLP在关系抽取中的应用
import openai

openai.api_key = "YOUR_API_KEY"

def extract_relationship(text):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"从文本中抽取关系：{text}\n",
        max_tokens=150
    )

    return response.choices[0].text.strip()

# 示例
text_to_extract_relationship = "苹果公司创始人是史蒂夫·乔布斯。"
relationship_result = extract_relationship(text_to_extract_relationship)

print("关系抽取结果:", relationship_result)

# 示例代码：NLP在跨文档上下文理解中的应用
import openai

openai.api_key = "YOUR_API_KEY"

def understand_cross_document_context(document1, document2):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"理解跨文档上下文：{document1}\n{document2}\n",
        max_tokens=150
    )

    return response.choices[0].text.strip()

# 示例
document1 = "关于气候变化的科学研究报告。"
document2 = "气候变化对全球经济的影响。"
cross_document_context_result = understand_cross_document_context(document1, document2)

print("跨文档上下文理解结果:", cross_document_context_result)