Hands-On NLTK Tutorial
The hands-on NLTK tutorial in the form of Jupyter notebooks
NLTK is one of the most popular Python packages for Natural Language Processing (NLP).
Index of Jupyter Notebooks
| Notebooks |
|---|
| 1.1 Downloading Libs and Testing That They Are Working Getting ready to start! |
| 1.2 Text Analysis Using nltk.text Extracting interesting data from a given text |
| 2.1 Deriving N-Grams from Text Creating n-grams (for language classification) |
| 2.2 Detecting Text Language by Counting Stop Words.ipynb A simple way to find out what language a text is written in |
| 2.3 Language Identifier Using Word Bigrams State-of-the-art language classifier |
| 3.1 Bigrams, Stemming and Lemmatizing NLTK makes bigrams, stemming and lemmatization super-easy |
| 3.2 Finding Unusual Words in Given Language Which words do not belong with the rest of the text? |
| 3.3 Creating a POS Tagger Creating a Parts Of Speech tagger |
| 3.4 Parts of Speech and Meaning Exploring awesome features offered by WordNet |
| 4.1 Name Gender Identifier Building a classifier that guesses the gender of a name |
| 4.2 Classifying News Documents into Categories Building a classifier that guesses the category of a news item |
| 5.1 Sentiment Analysis Is a movie review positive or negative? |
| 5.2 Sentiment Analysis with nltk.sentiment.SentimentAnalyzer and VADER tools More sentiment analysis! |
| 6.1 Twitter Stream (and Cleaning Tweets) Live-stream tweets from Twitter |
| 6.2 Twitter Search Search through past tweets |
| 7.1 NLTK with the Greek Script Using NLTK with foreign scripts |
| 8.1 The langdetect and langid Libraries Useful libraries for language identification |
| 8.2 Word2Vec (gensim) Google's Word2vec |
Meta
H. Z. Sababa — hb20007 — hzsababa@outlook.com
Distributed under the MIT license. See LICENSE for more information.
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The source to this hands on project, and all projects in this book, can be found here. Note that in this chapter we will load code directly from the local hard drive rather than through a webserver. Y
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Building A Sky For our hands on, we will create a new scene: a car that drives around on a large grassy plain under a starry sky. This is adapted from a series of great blog posts by Jerome, who also
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帧动画（Keyframe Animation）并不少见，就是播放一系列帧图片。但是帧动画加上手势（比如滑动），就会产生奇妙的效果。这份代码就是让手势来控制帧动画。滑动屏幕时，会根据屏幕滑动来产生动画效果（Scrolling Animation）。这种滑动产生动画的效果多用在App的介绍界面或者用户指引界面，比如 IFTTT，Mail Box等等以设计闻名的app，一般都将这种动画引入滑动用户指引界
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NLTK 会被自然地看作是具有栈结构的一系列层，这些层构建于彼此基础之上。那些熟悉人工语言（比如 Python）的文法和解析的读者来说，理解自然语言模型中类似的 —— 但更深奥的 —— 层不会有太大困难。 尽管 NLTK 附带了很多已经预处理（通常是手工地）到不同程度的全集，但是概念上每一层都是依赖于相邻的更低层次的处理。首先是断词；然后是为单词加上 标签；然后将成组的单词解析为语法元素，比如名词
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在本章中，我们将学习如何开始使用Natural Language Toolkit Package。 先决条件(Prerequisite) 如果我们想用自然语言处理来构建应用程序，那么上下文的变化会使其变得非常困难。 上下文因素影响机器如何理解特定句子。 因此，我们需要使用机器学习方法开发自然语言应用程序，以便机器也能理解人类理解上下文的方式。 要构建这样的应用程序，我们将使用名为NLTK（Natu
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NLTK库安装 pip install nltk 执行python并下载书籍： [root@centos #] python Python 2.7.11 (default, Jan 22 2016, 08:29:18) [GCC 4.2.1 Compatible Apple LLVM 7.0.2 (clang-700.1.81)] on darwin Type "help", "copyrig