lda2vec模型代码

lda2vec模型是一种结合了LDA(Latent Dirichlet Allocation)和word2vec的模型，它能够将文本中的主题和单词之间的关系结合起来。

代码实现可以参考以下链接：
https://github.com/cemoody/lda2vec
https://github.com/vi3k6i5/lda2vec

这些代码库是用Python编写的，并使用了gensim、numpy、scipy等库。

在使用之前，请确保已经安装这些库。

需要注意，该模型的训练可能需要大量的数据和高性能计算机，因此可能需要耗费较长时间。

相关问题

lda2vec模型代码举例

LDA2vec是一种将词嵌入与主题嵌入相结合的话题模型。它使用了LDA（Latent Dirichlet Allocation）和word2vec中的词嵌入技术。 下面是一个简单的LDA2vec模型代码示例:

from gensim.models import Lda2Vec

# Prepare the data
texts = [['this', 'is', 'a', 'good', 'example'],
         ['example', 'two'],
         ['this', 'is', 'example', 'three']]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Train the model
model = Lda2Vec(corpus, dictionary, size=100, window=5, min_count=1, workers=2)

# Get topic representations
topic_vectors = model.get_topics()

# Get word representations
word_vectors = model.get_word_vectors()

LDA2vec需要gensim库,请确保已经安装了这个库。

结合了LDA主题模型、Word2Vec词向量模型的TextRank关键词抽取算法Python代码

以下是结合了LDA主题模型、Word2Vec词向量模型的TextRank关键词抽取算法的Python代码：

import jieba
import gensim
from gensim import corpora, models
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity


def load_stopwords(path):
    """
    加载停用词
    :param path: 停用词文件路径
    :return: 停用词列表
    """
    stopwords = []
    with open(path, 'r', encoding='utf-8') as f:
        for line in f.readlines():
            stopwords.append(line.strip())
    return stopwords


def get_sentences(text):
    """
    使用jieba分句
    :param text: 文本内容
    :return: 句子列表
    """
    sentences = []
    for line in text.split('\n'):
        line = line.strip()
        if not line:
            continue
        for s in line.split('。'):
            s = s.strip()
            if not s:
                continue
            sentences.append(s)
    return sentences


def segment(sentence, stopwords):
    """
    使用jieba进行分词并去除停用词
    :param sentence: 句子
    :param stopwords: 停用词列表
    :return: 分词后的列表
    """
    words = []
    for word in jieba.cut(sentence):
        word = word.strip()
        if not word:
            continue
        if word not in stopwords:
            words.append(word)
    return words


def get_word2vec_model(text, size=100, window=5, min_count=5, workers=4):
    """
    训练Word2Vec模型
    :param text: 文本内容
    :param size: 词向量维度
    :param window: 窗口大小
    :param min_count: 最小词频
    :param workers: 线程数
    :return: Word2Vec模型
    """
    sentences = []
    for line in text.split('\n'):
        line = line.strip()
        if not line:
            continue
        sentences.append(segment(line, stopwords))
    model = gensim.models.Word2Vec(sentences, size=size, window=window, min_count=min_count, workers=workers)
    return model


def get_lda_model(text, num_topics=8, passes=10):
    """
    训练LDA主题模型
    :param text: 文本内容
    :param num_topics: 主题数
    :param passes: 迭代次数
    :return: LDA模型和语料库
    """
    sentences = []
    for line in text.split('\n'):
        line = line.strip()
        if not line:
            continue
        sentences.append(segment(line, stopwords))
    dictionary = corpora.Dictionary(sentences)
    corpus = [dictionary.doc2bow(sentence) for sentence in sentences]
    lda_model = models.ldamodel.LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary, passes=passes)
    return lda_model, corpus


def get_topic_word_matrix(lda_model, num_topics, num_words):
    """
    获取主题-词矩阵
    :param lda_model: LDA模型
    :param num_topics: 主题数
    :param num_words: 每个主题选取的关键词数
    :return: 主题-词矩阵
    """
    topic_word_matrix = np.zeros((num_topics, num_words))
    for i in range(num_topics):
        topic_words = lda_model.get_topic_terms(i, topn=num_words)
        for j in range(num_words):
            topic_word_matrix[i][j] = topic_words[j][0]
    return topic_word_matrix


def get_sentence_topic_vector(sentence, lda_model, dictionary, num_topics):
    """
    获取句子的主题向量
    :param sentence: 句子
    :param lda_model: LDA模型
    :param dictionary: 词典
    :param num_topics: 主题数
    :return: 句子的主题向量
    """
    sentence_bow = dictionary.doc2bow(segment(sentence, stopwords))
    topic_vector = np.zeros(num_topics)
    for topic, prob in lda_model[sentence_bow]:
        topic_vector[topic] = prob
    return topic_vector


def get_similarity_matrix(sentences, word2vec_model):
    """
    获取句子之间的相似度矩阵
    :param sentences: 句子列表
    :param word2vec_model: Word2Vec模型
    :return: 相似度矩阵
    """
    similarity_matrix = np.zeros((len(sentences), len(sentences)))
    for i in range(len(sentences)):
        for j in range(i+1, len(sentences)):
            sim = cosine_similarity([np.mean([word2vec_model[word] for word in segment(sentences[i], stopwords) if word in word2vec_model], axis=0)],
                                     [np.mean([word2vec_model[word] for word in segment(sentences[j], stopwords) if word in word2vec_model], axis=0)]).item()
            similarity_matrix[i][j] = sim
            similarity_matrix[j][i] = sim
    return similarity_matrix


def get_textrank_score(sentences, num_topics, lda_model, word2vec_model):
    """
    获取TextRank算法得分
    :param sentences: 句子列表
    :param num_topics: 主题数
    :param lda_model: LDA模型
    :param word2vec_model: Word2Vec模型
    :return: 句子得分列表
    """
    dictionary = lda_model.id2word
    num_words = 20
    topic_word_matrix = get_topic_word_matrix(lda_model, num_topics, num_words)
    sentence_topic_vectors = np.zeros((len(sentences), num_topics))
    for i in range(len(sentences)):
        sentence_topic_vectors[i] = get_sentence_topic_vector(sentences[i], lda_model, dictionary, num_topics)
    similarity_matrix = get_similarity_matrix(sentences, word2vec_model)

    # TextRank算法迭代
    max_iter = 100
    d = 0.85
    scores = np.ones(len(sentences))
    for i in range(max_iter):
        tmp_scores = np.zeros(len(sentences))
        for j in range(len(sentences)):
            tmp_scores[j] = (1 - d) + d * np.sum([similarity_matrix[j][k] * scores[k] for k in range(len(sentences))])
        scores = tmp_scores

    # 合并TextRank和主题模型得分
    final_scores = np.zeros(len(sentences))
    for i in range(len(sentences)):
        for j in range(num_topics):
            final_scores[i] += topic_word_matrix[j].tolist().count(i) * sentence_topic_vectors[i][j]
    final_scores = d * final_scores + (1 - d) * scores

    return final_scores


# 加载停用词
stopwords = load_stopwords('stopwords.txt')

# 加载文本
with open('text.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# 分句
sentences = get_sentences(text)

# 训练Word2Vec模型
word2vec_model = get_word2vec_model(text)

# 训练LDA主题模型
lda_model, corpus = get_lda_model(text)

# 获取TextRank算法得分
num_topics = 8
scores = get_textrank_score(sentences, num_topics, lda_model, word2vec_model)

# 按得分排序，获取关键词
num_keywords = 10
keywords = []
idx = np.argsort(scores)[::-1][:num_keywords]
for i in idx:
    keywords.append(sentences[i])

print(keywords)

其中，text.txt为待处理的文本文件，stopwords.txt为停用词文件，需要自行准备。代码中num_topics、num_words、num_keywords等参数需要根据实际情况进行调整。