帮我使用python代码实现文档查重，关键词提取，中心思想提取等功能，并打出来

好的，我可以使用自然语言处理技术实现您所需的功能。以下是Python代码实现文档查重的示例：

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def preprocess_text(text):
    # 去除标点符号
    tokens = [word.lower() for word in word_tokenize(text) if word.isalpha()]
    # 去除停用词
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if not word in stop_words]
    # 词形还原
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(word) for word in tokens]
    # 词干提取
    stemmer = PorterStemmer()
    tokens = [stemmer.stem(word) for word in tokens]
    return ' '.join(tokens)

def check_duplicates(original_text, new_text):
    # 预处理原始文本和新文本
    original_text = preprocess_text(original_text)
    new_text = preprocess_text(new_text)
    # 创建词袋模型，并计算相似度
    vectorizer = CountVectorizer().fit_transform([original_text, new_text])
    similarity_score = cosine_similarity(vectorizer)[0][1]
    # 如果相似度 >= 0.8，则认为新文本与原始文本重复
    if similarity_score >= 0.8:
        return True
    else:
        return False

使用示例：

original_text = "This is an original text."
new_text = "This is a new text that is very similar to the original text."

if check_duplicates(original_text, new_text):
    print("新文本与原始文本重复！")
else:
    print("新文本与原始文本不重复。")

关键词提取和中心思想提取可以使用TextRank算法来实现。以下是Python代码实现关键词提取和中心思想提取的示例：

import networkx as nx
from collections import defaultdict

def extract_keywords_and_central_ideas(text):
    # 预处理文本
    text = preprocess_text(text)
    # 将文本分成句子
    sentences = sent_tokenize(text)
    # 提取关键词
    keywords = []
    for sentence in sentences:
        words = word_tokenize(sentence)
        pos_tags = nltk.pos_tag(words)
        for word, pos in pos_tags:
            if pos.startswith('N') or pos.startswith('J'):
                keywords.append(word)
    # 提取中心思想
    tr = nx.Graph()
    for sentence in sentences:
        words = word_tokenize(sentence)
        for i, word1 in enumerate(words):
            for j, word2 in enumerate(words):
                if i != j:
                    tr.add_edge(word1, word2)
    pr = nx.pagerank(tr)
    central_ideas = defaultdict(float)
    for word, pr_value in pr.items():
        for sentence in sentences:
            if word in sentence:
                central_ideas[sentence] += pr_value
    central_ideas = sorted(central_ideas.items(), key=lambda x: x[1], reverse=True)[:3]
    central_ideas = [x[0] for x in central_ideas]
    return keywords, central_ideas

使用示例：

text = "This text is about natural language processing. It defines what natural language processing is and what its applications are. It also provides an overview of the techniques used in natural language processing."
keywords, central_ideas = extract_keywords_and_central_ideas(text)
print("关键词：", keywords)
print("中心思想：", central_ideas)