doc2vec

词袋模型（Bag of Words Model） 词袋模型的概念 先来看张图，从视觉上感受一下词袋模型的样子。 词袋模型看起来像一个口袋把所有词都装进去，但却不完全如此。在自然语言处理和信息检索中作为一种简单假设，词袋模型把文本（段落或者文档）被看作是无序的词汇集合，忽略语法甚至是单词的顺序，把每一个单词都进行统计，同时计算每个单词出现的次数，常被用在文本分类中，如贝叶斯算法、LDA 和 LSA等。 动手实战词袋模型 （1）词袋模型 本例中，我们自己动手写代码看看词袋模型是如何操作的。 首先，引入 jieba 分词器、语料和停用词（标点符号集合，自己可以手动添加或者用一个文本字典代替）。 import jieba #定义停用词、标点符号 punctuation = ["，","。", "：", "；", "？"] #定义语料 content = ["机器学习带动人工智能飞速的发展。", "深度学习带动人工智能飞速的发展。", "机器学习和深度学习带动人工智能飞速的发展。" ] 接下来，我们先对语料进行分词操作，这里用到 lcut() 方法： #分词 segs_1 = [jieba.lcut(con) for con in content] print(segs_1) 得到分词后的结果如下： [['机器', '学习', '带动', '人工智能', '飞速', '的', '发展', '

I am trying doc2vec for 600000 rows of sentences and my code is below: model = gensim.models.doc2vec.Doc2Vec(size= 100, min_count = 5,window=4, iter = 50, workers=cores) model.build_vocab(res) model.train(res, total_examples=model.corpus_count, epochs=model.iter) #len(res) = 663406 #length of unique words 15581 print(len(model.wv.vocab)) #length of doc vectors is 10 len(model.docvecs) # each of length 100 len(model.docvecs[1]) How do I interpret this result? why is the length of vector only 10 with each of size 100? when the length of 'res' is 663406, it does not make sense. I know something

I am new to doc2vec. I was initially trying to understand doc2vec and mentioned below is my code that uses Gensim. As I want I get a trained model and document vectors for the two documents. However, I would like to know the benefits of retraining the model in several epoches and how to do it in Gensim? Can we do it using iter or alpha parameter or do we have to train it in a seperate for loop ? Please let me know how I should change the following code to train the model for 20 epoches. Also, I am interested in knowing is the multiple training iterations are needed for word2vec model as well.

i am going thorugh this paper http://cs.stanford.edu/~quocle/paragraph_vector.pdf and it states that " Theparagraph vector and word vectors are averaged or concatenated to predict the next word in a context. In the experiments, we use concatenation as the method to combine the vectors." How does concatenation or averaging work? example (if paragraph 1 contain word1 and word2): word1 vector =[0.1,0.2,0.3] word2 vector =[0.4,0.5,0.6] concat method does paragraph vector = [0.1+0.4,0.2+0.5,0.3+0.6] ? Average method does paragraph vector = [(0.1+0.4)/2,(0.2+0.5)/2,(0.3+0.6)/2] ? Also from this

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am learning Doc2Vec model from gensim library and using it as follows: class MyTaggedDocument(object): def __init__(self, dirname): self.dirname = dirname def __iter__(self): for fname in os.listdir(self.dirname): with open(os.path.join(self.dirname, fname),encoding='utf-8') as fin: print(fname) for item_no, sentence in enumerate(fin): yield LabeledSentence([w for w in sentence.lower().split() if w in stopwords.words('english')], [fname.split('.')[0].strip() + '_%s' % item_no]) sentences = MyTaggedDocument(dirname) model = Doc2Vec