我现在有一个已经分过词并且去除过停用词的名为“2015.txt”的文档，其内容为“词：词频”形式的字典。要对“2015.txt”基于TF-IDF抽取关键词，另有一个文档，其中包含了很多篇子文档，每篇子文档都以“本篇文章为：”开头，要将这个文档作为语料库，提取“2015.txt”的关键词，用词在“2015.txt”中出现的次数除以语料库中包含该词的文档数表示tf-idf，请你用python实现，请直接从读取语料库这一步开始

好的，根据您的要求，我建议您使用Python中的gensim库来实现基于TF-IDF的关键词抽取。下面是代码实现的步骤：

1.读取语料库

from gensim import corpora
from gensim import models

# 读入语料库
corpus = []
with open('corpus.txt', 'r', encoding='utf-8') as f:
    doc = []
    for line in f:
        if line.startswith('本篇文章为：'):
            corpus.append(doc)
            doc = []
        else:
            doc.append(line.strip())
    corpus.append(doc)
corpus = corpus[1:]  # 去掉第一个空文档

2.对语料库进行分词，并去除停用词

import jieba
import jieba.analyse
import jieba.posseg as pseg

# 加载停用词表
stop_words = []
with open('stopwords.txt', 'r', encoding='utf-8') as f:
    for line in f:
        stop_words.append(line.strip())

# 分词
for doc in corpus:
    for i in range(len(doc)):
        words = []
        for word, flag in pseg.cut(doc[i]):
            if flag.startswith('n') and word not in stop_words:
                words.append(word)
        doc[i] = words

3.将每篇文档转换为词袋模型，并计算TF-IDF

# 构建词典
dictionary = corpora.Dictionary([word for doc in corpus for word in doc])

# 将每篇文档转换为词袋模型
corpus_bow = [dictionary.doc2bow(doc) for doc in corpus]

# 计算TF-IDF
tfidf_model = models.TfidfModel(corpus_bow)
corpus_tfidf = tfidf_model[corpus_bow]

4.抽取关键词

# 读入2015.txt
keywords = {}
with open('2015.txt', 'r', encoding='utf-8') as f:
    for line in f:
        word, freq = line.strip().split('：')
        keywords[word] = int(freq)

# 计算每个关键词的TF-IDF值
for word, freq in keywords.items():
    word_id = dictionary.token2id.get(word)
    if word_id is None:
        continue
    tfidf_sum = sum([weight for doc in corpus_tfidf if word_id in [word_id for word_id, weight in doc]])
    tfidf = tfidf_sum / len(corpus_tfidf)
    keywords[word] = freq * tfidf

# 按照TF-IDF值从大到小排序，取前10个关键词
top_keywords = sorted(keywords.items(), key=lambda x: x[1], reverse=True)[:10]

这样就可以得到TF-IDF抽取的关键词了。代码中涉及到的文件路径和停用词表需要根据具体情况进行修改。