Getting Started with Word2Vec

1. Source by Google

Project with Code: https://code.google.com/archive/p/word2vec/

Blog: Learning Meaning Behind Words

Paper:

  1. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.
  2. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.
  3. Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of NAACL HLT, 2013.
  4. Tomas Mikolov, Quoc V. Le, Ilya Sutskever. Exploiting Similarities among Languages for Machine Translation
  5. NIPS DeepLearning Workshop NN for Text by Tomas Mikolov and etc. https://docs.google.com/file/d/0B7XkCwpI5KDYRWRnd1RzWXQ2TWc/edit

2. Best explaination

Best explained with original models, optimizing methods, Back-propagation background and Word Embedding Visual Inspector

paper: word2vec Parameter Learning Explained

Slides: Word Embedding Explained and Visualized

Youtube Video: Word Embedding Explained and Visualized – word2vec and wevi

Demo: wevi: word embedding visual inspector

3. Word2Vec Tutorials

Word2Vec Tutorial by Chris McCormick

Chris McCormick http://mccormickml.com/

Note: skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details

Word2Vec Tutorial – The Skip-Gram Model

Word2Vec Tutorial Part 2 – Negative Sampling

Alex Minnaar’s Tutorials

Alex Minnaar http://alexminnaar.com/

Word2Vec Tutorial Part I: The Skip-Gram Model

Word2Vec Tutorial Part II: The Continuous Bag-of-Words Model

4. Learning by Coding

Distributed Representations of Sentences and Documents http://nbviewer.jupyter.org/github/fbkarsdorp/doc2vec/blob/master/doc2vec.ipynb

An Anatomy of Key Tricks in word2vec project with examples http://nbviewer.jupyter.org/github/dolaameng/tutorials/blob/master/word2vec-abc/poc/pyword2vec_anatomy.ipynb

Python Word2Vec by Gensim related articles

  1. Deep learning with word2vec and gensim, Part One
  2. Word2vec in Python, Part Two: Optimizing
  3. Parallelizing word2vec in Python, Part Three
  4. Gensim word2vec document: models.word2vec – Deep learning with word2vec
  5. Word2vec Tutorial by Radim Řehůřek (Note: Simple but very powerful tutorial for word2vec model training in gensim.)

5. Ohter Word2Vec Resources

Word2Vec Resources by Chris McCormick

Posted by TextProcessing

References

  1. https://textprocessing.org/getting-started-with-word2vec

Getting Started with Word2Vec的更多相关文章

  1. word2vec 中的数学原理详解

    word2vec 是 Google 于 2013 年开源推出的一个用于获取 word vector 的工具包,它简单.高效,因此引起了很多人的关注.由于 word2vec 的作者 Tomas Miko ...

  2. Java豆瓣电影爬虫——使用Word2Vec分析电影短评数据

    在上篇实现了电影详情和短评数据的抓取.到目前为止,已经抓了2000多部电影电视以及20000多的短评数据. 数据本身没有规律和价值,需要通过分析提炼成知识才有意义.抱着试试玩的想法,准备做一个有关情感 ...

  3. word2vec参数调整 及lda调参

     一.word2vec调参   ./word2vec -train resultbig.txt -output vectors.bin -cbow 0 -size 200 -window 5 -neg ...

  4. [Algorithm & NLP] 文本深度表示模型——word2vec&doc2vec词向量模型

    深度学习掀开了机器学习的新篇章,目前深度学习应用于图像和语音已经产生了突破性的研究进展.深度学习一直被人们推崇为一种类似于人脑结构的人工智能算法,那为什么深度学习在语义分析领域仍然没有实质性的进展呢? ...

  5. Word2Vec 使用总结

    word2vec 是google 推出的做词嵌入(word embedding)的开源工具. 简单的说,它在给定的语料库上训练一个模型,然后会输出所有出现在语料库上的单词的向量表示,这个向量称为&qu ...

  6. Word2vec多线程(tensorflow)

    workers = [] for _ in xrange(opts.concurrent_steps): t = threading.Thread(target=self._train_thread_ ...

  7. Word2vec 模型载入(tensorflow)

    opts = Options() with tf.Graph().as_default(), tf.Session() as session: model = Word2Vec(opts, sessi ...

  8. Forward-backward梯度求导(tensorflow word2vec实例)

    考虑不可分的例子         通过使用basis functions 使得不可分的线性模型变成可分的非线性模型 最常用的就是写出一个目标函数 并且使用梯度下降法 来计算     梯度的下降法的梯度 ...

  9. Tensorflow word2vec编译运行

    Word2vec 更完整版本(非demo)的代码在 tensorflow/models/embedding/     首先需要安装bazel 来进行编译 bazel可以下载最新的binary安装文件, ...

  10. 中英文维基百科语料上的Word2Vec实验

    最近试了一下Word2Vec, GloVe 以及对应的python版本 gensim word2vec 和 python-glove,就有心在一个更大规模的语料上测试一下,自然而然维基百科的语料进入了 ...

随机推荐

  1. ping vs telnet, what is the difference between them and when to use which?

    Ping is an ICMP protocol. Basically any system with TCP/IP could respond to ICMP calls if they were ...

  2. Easyui datagrid 数据表格 表格列头右键菜单选择展示列 JS

    Easyui ,数据表格加载出来以后,在表格头右键,会有显示筛选的功能: 如图: 然后可以取消勾选,就变成下面这个样子: 功能的实现是通过重写了easyui 的 $.fn.datagrid.defau ...

  3. promise 的学习

    promise 是为了解决异步操作的顺序问题而产生的 特性 promise 的实例一旦创建就会执行里面的异步操作 promise 的实例状态一旦改变就变成凝固的了, 无法再对其作出修改,  (不明白为 ...

  4. Map集合转成json数据

    maven项目需要导入一下依赖: <dependency> <groupId>net.sf.json-lib</groupId> <artifactId> ...

  5. 转载:Android RecyclerView 使用完全解析 体验艺术般的控件

    转自:https://blog.csdn.net/lmj623565791/article/details/45059587

  6. php json 中文不转义 & 转义为中文

    JSON_UNESCAPED_UNICODE private function decodeUnicode($str){ return preg_replace_callback('/\\\\u([0 ...

  7. python tkinter messagebox

    """messagebox消息框""" import tkinter as tk #导入messagebox import tkinter. ...

  8. RN启动报错,环境相关问题

    启动RN的时候刚开始报错: The request was denied by service delegate (SBMainWorkspace) for reason: Security (&qu ...

  9. Virtual Memory is deprecated in Redis 2.4

    在读一个源码的讲解的文章时或者读一本关于某个技术的数据集时,可能书籍的讲解是滞后的,就是没有更上最新的代码,那么就要注意了WARNING! Virtual Memory is deprecated i ...

  10. windows下复制文件报错“文件名对目标文件夹可能过长 。您可以缩短文件名并重试,或者......”

    我将一个路径下文件夹复制到另一个路径下时,出现了报错,报错图片如下: 然后查资料发现: 1.文件名长度最大为255个英文字符,其中包括文件扩展名在内.一个汉字相当于两个英文字符.2.文件的全路径名长度 ...