python问答系统代码实现

要实现一个Python问答系统，需要先搭建一个语料库，然后用自然语言处理（NLP）技术提取问题和答案，最后将其封装为一个可交互的界面。

以下是一个简单的Python问答系统示例：

import nltk
from nltk.corpus import wordnet
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import WordNetLemmatizer

# 初始化Lemmatizer
lemmatizer = WordNetLemmatizer()

# 读取语料库
corpus = open('corpus.txt').read()

# 将文本分成句子
sentences = sent_tokenize(corpus)

# 将句子分成单词
words = word_tokenize(corpus)

# 将每个单词还原为原型
lemmatized_words = [lemmatizer.lemmatize(word) for word in words]

# 获取同义词
synonyms = []
for word in lemmatized_words:
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            synonyms.append(lemma.name())

# 去除重复的同义词
synonyms = set(synonyms)

# 定义回答问题的函数
def answer_question(question):
    # 将问题分成单词
    words = word_tokenize(question)

    # 将每个单词还原为原型
    lemmatized_words = [lemmatizer.lemmatize(word) for word in words]

    # 获取问题的同义词
    question_synonyms = []
    for word in lemmatized_words:
        for syn in wordnet.synsets(word):
            for lemma in syn.lemmas():
                question_synonyms.append(lemma.name())

    # 去除重复的同义词
    question_synonyms = set(question_synonyms)

    # 找到问题和答案之间的最佳匹配
    best_match = ""
    max_overlap = 0
    for sentence in sentences:
        # 将句子分成单词
        words = word_tokenize(sentence)

        # 将每个单词还原为原型
        lemmatized_words = [lemmatizer.lemmatize(word) for word in words]

        # 获取句子的同义词
        sentence_synonyms = []
        for word in lemmatized_words:
            for syn in wordnet.synsets(word):
                for lemma in syn.lemmas():
                    sentence_synonyms.append(lemma.name())

        # 去除重复的同义词
        sentence_synonyms = set(sentence_synonyms)

        # 计算问题和句子之间的重叠
        overlap = len(question_synonyms.intersection(sentence_synonyms))

        # 如果重叠大于最大重叠，则更新最佳匹配
        if overlap > max_overlap:
            max_overlap = overlap
            best_match = sentence

    return best_match

# 与用户交互
while True:
    question = input("请输入您的问题： ")
    answer = answer_question(question)
    print(answer)

注意，这只是一个简单的示例，如果您需要实现一个更复杂的问答系统，需要更多的自然语言处理技术和算法。