要求用户输入一个问题和一段材料，用python语言实现在给定材料中抽取问题答案的功能

可以使用自然语言处理库spaCy来实现这个功能。首先需要安装spaCy库和其英文语言模型。

!pip install spacy
!python -m spacy download en_core_web_sm

接下来，可以使用以下代码来实现在给定材料中抽取问题答案的功能。

import spacy

nlp = spacy.load("en_core_web_sm")

def extract_answer(question, passage):
    question_doc = nlp(question)
    passage_doc = nlp(passage)
    
    # 找到与问题最相关的句子
    sentences = [sent.text.strip() for sent in passage_doc.sents]
    sentence_scores = []
    for sentence in sentences:
        sentence_doc = nlp(sentence)
        score = question_doc.similarity(sentence_doc)
        sentence_scores.append(score)
    best_sentence = sentences[sentence_scores.index(max(sentence_scores))]
    
    # 找到最相关句子中的答案
    best_sentence_doc = nlp(best_sentence)
    answer = ""
    for ent in best_sentence_doc.ents:
        if ent.label_ in ["DATE", "TIME", "PERSON", "NORP", "ORG", "GPE", "LOC", "PRODUCT", "EVENT", "WORK_OF_ART"]:
            answer = ent.text
            break
    if answer:
        return answer
    else:
        for token in best_sentence_doc:
            if token.pos_ in ["NOUN", "PROPN"] and not token.is_stop:
                answer = token.text
                break
    return answer

这个函数接受两个参数：问题和材料。它首先使用spaCy库将它们转换为文档对象。然后它将材料中的每个句子与问题进行比较，并找到最相关的句子。最后，它在最相关的句子中查找实体或名词作为答案，并将其返回。如果无法找到答案，则返回空字符串。

接下来可以测试一下这个函数。

question = "What is the capital of France?"
passage = "France is a country located in Western Europe. Its capital is Paris and its currency is the Euro."

answer = extract_answer(question, passage)
print(answer) # 输出：Paris

可以看到，函数成功地从材料中提取出了问题的答案。