word2vec

In LDA model generates different topics everytime i train on the same corpus , by setting the np.random.seed(0) , the LDA model will always be initialized and trained in exactly the same way. Is it the same for the Word2Vec models from gensim ? By setting the random seed to a constant, would the different run on the same dataset produce the same model? But strangely, it's already giving me the same vector at different instances. >>> from nltk.corpus import brown >>> from gensim.models import Word2Vec >>> sentences = brown.sents()[:100] >>> model = Word2Vec(sentences, size=10, window=5, min

Given a model, e.g. from gensim.models.word2vec import Word2Vec documents = ["Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey"] texts = [d.lower().split() for d in documents] w2v_model =

关于word2vec的理解，推荐文章 https://www.cnblogs.com/guoyaohua/p/9240336.html 代码在jupyter notebook下运行。 from __future__ import print_function #表示不管哪个python版本，使用最新的print语法 import collections import math import numpy as np # import os import random import tensorflow as tf import zipfile from matplotlib import pylab from six.moves import range from six.moves.urllib.request import urlretrieve from sklearn.manifold import TSNE %matplotlib inline 下载text8.zip文件，这个文件包含了大量单词。官方地址为 http://mattmahoney.net/dc/text8.zip filename='text8.zip' def read_data(filename): """Extract the first file enclosed in a zip file as a

I am new to tensorflow and to word2vec. I just studied the word2vec_basic.py which trains the model using Skip-Gram algorithm. Now I want to train using CBOW algorithm. Is it true that this can be achieved if I simply reverse the train_inputs and train_labels ? I think CBOW model can not simply be achieved by flipping the train_inputs and the train_labels in Skip-gram because CBOW model architecture uses the sum of the vectors of surrounding words as one single instance for the classifier to predict. E.g., you should use [the, brown] together to predict quick rather than using the to predict

一、背景 自然语言处理就是要让计算机理解人类的语言，至于到目前为止，计算机是否真的理解的人类的语言，这是一个未知之数，我的理解是目前为止并没有懂得人类语言，只是查表给出一个最大概率的回应而已。那么自然语言处理（NLP）包括哪些领域的东西呢？文本分类（如：垃圾邮件分类、情感分析）、机器翻译、摘要、文法分析、分词、词性标注、实体识别(NER)、语音识别等等，都是NLP要解的问题。那么这些解了这些问题，计算机是否真的懂得人类语言的含义，现在还未知，本片文章不过多的展开讨论。语言的单位是词，那么计算机是如何来表示词的，用什么技术来表示一个词，就可以让计算机理解词的含义呢？本篇博客将进行详细的讨论，从bool模型，到向量空间模型、到各种word embedding（word2vec、elmo、GPT、BERT） 二、原始时代 在Deeplearning之前，表示一个词，并没有一个约定俗成的办法，如何表示，取决于想解决的任务。 1、Bool模型 下面有两句话，求文本相似度。 我喜欢张国荣 你喜欢刘德华 那么，布尔模型比较简单粗暴，出现了词所在维度为1，没出现的所在维度为0，如下图： 然后求两个向量的cosine即可。 在bool模型中，由于特征值只有1和0两个取值，不能很好的反应特征项在文本中的重要程度。 2、VSM（向量空间模型） Bool模型其实可以看做是VSM的特例

I want to visualize a word2vec created from gensim library. I tried sklearn but it seems I need to install a developer version to get it. I tried installing the developer version but that is not working on my machine . Is it possible to modify this code to visualize a word2vec model ? tsne_python You don't need a developer version of scikit-learn - just install scikit-learn the usual way via pip or conda . To access the word vectors created by word2vec simply use the word dictionary as index into the model: X = model[model.wv.vocab] Following is a simple but complete code example which loads

I am using pre-trained Google news dataset for getting word vectors by using Gensim library in python model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) After loading the model I am converting training reviews sentence words into vectors #reading all sentences from training file with open('restaurantSentences', 'r') as infile: x_train = infile.readlines() #cleaning sentences x_train = [review_to_wordlist(review,remove_stopwords=True) for review in x_train] train_vecs = np.concatenate([buildWordVector(z, n_dim) for z in x_train]) During word2Vec process i

BACKGROUND I have vectors with some sample data and each vector has a category name (Places,Colors,Names). ['john','jay','dan','nathan','bob'] -> 'Names' ['yellow', 'red','green'] -> 'Colors' ['tokyo','bejing','washington','mumbai'] -> 'Places' My objective is to train a model that take a new input string and predict which category it belongs to. For example if a new input is "purple" then I should be able to predict 'Colors' as the correct category. If the new input is "Calgary" it should predict 'Places' as the correct category. APPROACH I did some research and came across Word2vec . This