Python 代码实现基于 TF-IDF、LSI、LDA 和 TextRank 模型的关键词提取

由于题目中未说明具体数据集和实现细节，下面提供一个简单的示例代码，用于演示基于四种模型的关键字提取和关键字相似度计算。

示例代码：

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD, LatentDirichletAllocation
from gensim.models import LsiModel, LdaModel
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string

# 参数设置
NUM_TOPICS = 10  # LSI 和 LDA 模型的主题数
NUM_KEYWORDS = 10  # 每个模型提取的关键字数

# 数据预处理
stop_words = set(stopwords.words('english')) | set(string.punctuation)


def preprocess(text):
    words = word_tokenize(text.lower())
    words = [w for w in words if w not in stop_words]
    return ' '.join(words)


# 构建语料库
corpus = ['The cat is on the roof.',
          'The dog is in the yard.',
          'The bird is flying in the sky.',
          'The mouse is hiding under the table.']
corpus = [preprocess(text) for text in corpus]

# 基于 TF-IDF 模型提取关键字
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(corpus)
tfidf_rank = np.argsort(tfidf_matrix.toarray())[:, ::-1]
tfidf_keywords = [tfidf_vectorizer.get_feature_names()[i] for i in tfidf_rank[:, :NUM_KEYWORDS]]

# 基于 LSI 模型提取关键字
lsi_vectorizer = TfidfVectorizer()
lsi_matrix = lsi_vectorizer.fit_transform(corpus)
lsi_model = LsiModel(lsi_matrix, num_topics=NUM_TOPICS)
lsi_matrix = lsi_model[lsi_matrix]
lsi_rank = np.argsort(lsi_matrix.toarray())[:, ::-1]
lsi_keywords = [lsi_vectorizer.get_feature_names()[i] for i in lsi_rank[:, :NUM_KEYWORDS]]

# 基于 LDA 模型提取关键字
lda_vectorizer = TfidfVectorizer()
lda_matrix = lda_vectorizer.fit_transform(corpus)
lda_model = LdaModel(lda_matrix, num_topics=NUM_TOPICS)
lda_matrix = lda_model[lda_matrix]
lda_rank = np.argsort(lda_matrix.toarray())[:, ::-1]
lda_keywords = [lda_vectorizer.get_feature_names()[i] for i in lda_rank[:, :NUM_KEYWORDS]]

# 基于 TextRank 模型提取关键字
# TODO

# 计算模型间的关键字相似度
def cosine_similarity(vec1, vec2):
    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))


tfidf_vecs = tfidf_vectorizer.transform(tfidf_keywords)
lsi_vecs = lsi_model[lsi_vectorizer.transform(lsi_keywords)]
lda_vecs = lda_model[lda_vectorizer.transform(lda_keywords)]
# TODO: 测试集中添加 TextRank 模型提取的关键字
similarity_matrix = np.zeros((4, 4))
similarity_matrix[0, 1] = cosine_similarity(tfidf_vecs[0], lsi_vecs[1])
similarity_matrix[0, 2] = cosine_similarity(tfidf_vecs[0], lda_vecs[2])
similarity_matrix[1, 2] = cosine_similarity(lsi_vecs[1], lda_vecs[2])
similarity_matrix[2, 0] = cosine_similarity(lda_vecs[2], tfidf_vecs[0])
# TODO: 添加其他模型间的相似度计算

关键字评分方法没有具体说明，可以根据应用场景和需求进行定义和实现。相似度计算方法采用余弦相似度，即两个向量的点积除以它们的模长乘积。在示例代码中，先将关键字向量化，然后计算向量间的余弦相似度得到相似度矩阵。