word2vec

I am trying to use the freebase word embeddings released by Google, but I have a hard time getting the words from the freebase name. model = gensim.models.Word2Vec.load_word2vec_format('freebase-vectors-skipgram1000.bin',binary=True) model.vocab.keys()[:10] Out[22]: [u'/m/026tg5z', u'/m/018jz8', u'/m/04klsk', u'/m/08gd39', u'/m/0kt94', u'/m/05mtf0t', u'/m/05tjjb', u'/m/01m3vn', u'/m/0h7p35', u'/m/03ggvg3'] Does anyone know if it exist some kind of table to map the freebase representations into the words they represent ? Regards, Hedi Someone has actually done a nice thing for us all and mapped

I am trying to properly understand the batch_input and batch_labels from the tensorflow "Vector Representations of Words" tutorial. For instance, my data 1 1 1 1 1 1 1 1 5 251 371 371 1685 ... ... starts with skip_window = 2 # How many words to consider left and right. num_skips = 1 # How many times to reuse an input to generate a label. Then the generated input array is: bach_input = 1 1 1 1 1 1 5 251 371 .... This makes sense, starts from after 2 (= window size) and then continuous. The labels: batch_labels = 1 1 1 1 1 1 251 1 1685 371 589 ... I don't understand these labels very well. There

论文 Distributed Representations of Words and Phrases and their Compositionality Natural Language Processing (almost) from Scratch Efficient estimation of word representations in vector space word2vec Parameter Learning Explained 官网 word2vec API models.word2vec – Word2vec embeddings 语料 搜狗实验室 Pre-trained word vectors of 30+ languages 中文维基分词语料：链接 https://pan.baidu.com/s/1qXKIPp6 密码 kade 腾讯AI Lab开源大规模高质量中文词向量数据，800万中文词随你用 实战 # 加载包 from gensim.models import Word2Vec from gensim.models.word2vec import LineSentence import logging import itertools import gensim from gensim import utils # 训练模型 sentences

I want to construct word embeddings for documents using word2vec tool. I know how to find a vector embedding corresponding to a single word(unigram). Now, I want to find a vector for a bigram. Is it possible to do using word2vec? If yes, how? The following snippet will get you the vector representation of a bigram. Note that the bigram you want to convert to a vector needs to have an underscore instead of a space between the words, e.g. bigram2vec(unigrams, "this report") is wrong, it should be bigram2vec(unigrams, "this_report") . For more details on generating the unigrams, please see the

I used VGG 16-Layer Caffe model for image captions and I have several captions per image. Now, I want to generate a sentence from those captions (words). I read in a paper on LSTM that I should remove the SoftMax layer from the training network and provide the 4096 feature vector from fc7 layer directly to LSTM. I am new to LSTM and RNN stuff. Where should I begin? Is there any tutorial showing how to generate sentence by sequence labeling? AFAIK the master branch of BVLC/caffe does not yet support a recurrent layer architecture. You should pull branch recurrent from jeffdonahue/caffe . This

I have a Word2Vec model which is trained in Gensim . How can I use it in Tensorflow for Word Embeddings . I don't want to train Embeddings from scratch in Tensorflow. Can someone tell me how to do it with some example code? Let's assume you have a dictionary and inverse_dict list, with index in list corresponding to most common words: vocab = {'hello': 0, 'world': 2, 'neural':1, 'networks':3} inv_dict = ['hello', 'neural', 'world', 'networks'] Notice how the inverse_dict index corresponds to the dictionary values. Now declare your embedding matrix and get the values: vocab_size = len(inv_dict)

I want to use a pre-trained word2vec model, but I don't know how to load it in python. This file is a MODEL file (703 MB). It can be downloaded here: http://devmount.github.io/GermanWordEmbeddings/ just for loading import gensim # Load pre-trained Word2Vec model. model = gensim.models.Word2Vec.load("modelName.model") now you can train the model as usual. also, if you want to be able to save it and retrain it multiple times, here's what you should do model.train(//insert proper parameters here//) """ If you don't plan to train the model any further, calling init_sims will make the model much

Word2Vec的主要目的适用于词的特征提取，然后我们就可以用LSTM等神经网络对这些特征进行训练。 由于机器学习无法直接对文本信息进行有效的处理，机器学习只对数字，向量，多维数组敏感，所以在进行文本训练之前还要做一些转化工作，Word2Vec就是担负此重任的有效工具，当然还有其他工具，就不再说明。本次只是简单介绍Word2Vec的工作原理，想要详细理解还请看一下文章最后分享的链接。 Word2Vec工作过程 1.建立字典，每个词生成 one-hot 向量 Word个数为 n ，产生 n 维向量，第i 个 word 的向量为（0, 0, 0,…. 1, 0, 0, 0, 0）其中1的位置在向量的第i个位置上。 　　　 2.训练数据集构建 我门可以使用长度为4的滑动窗口进行取“词对”，如下图： 3.建立简单的神经网络 建立神经网络的真正意义在于要学到当前词是通过何种向量映射到其它词的。最后这个向量才是能够作为文本学习的特征向量。 Word2Vec本身不具有多大的学习作用，但它产生的词映射向量在当前的技术看来是作为机器学习语言的前提。如下图，我们所需要的就是中间的神经元模型： 4 . 生成最终 Vect 训练 model 特征提取，每个 one-hot 对应一个300d向量如下图 生成最终 look up word table Word2Vec 特点 1.利用上下文 (context)