Python库中还有哪些可以实现TF-IDF、TextRank和LSA三种算法的程序包，并通过实例实现关键词提取。

可以使用Python的sklearn库中的TfidfVectorizer、CountVectorizer和TruncatedSVD实现TF-IDF、TextRank和LSA算法。以下是一个示例代码：

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import TruncatedSVD
from textrankr import TextRank

# 使用TfidfVectorizer实现TF-IDF算法
corpus = ['This is the first document.', 'This is the second document.', 'And this is the third one.', 'Is this the first document?']
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(tfidf.toarray())

# 使用CountVectorizer和TextRank实现TextRank算法
corpus = ['This is the first document.', 'This is the second document.', 'And this is the third one.', 'Is this the first document?']
vectorizer = CountVectorizer()
tf = vectorizer.fit_transform(corpus)
tr = TextRank(tf.toarray())
keywords = tr.summarize(2)
print(keywords)

# 使用TruncatedSVD实现LSA算法
corpus = ['This is the first document.', 'This is the second document.', 'And this is the third one.', 'Is this the first document?']
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit_transform(corpus)
svd = TruncatedSVD(n_components=2)
lsa = svd.fit_transform(tfidf)
print(lsa)

当然，以上只是简单示例，具体实现还需要根据实际需求进行调整。