- text = nltk.Text(tokens) Quitting Python Quit quit Part-of-Speech Codes CC Coordinating conjunction CD Cardinal number DT Determiner EX Existential there FW Foreign word IN Preposition or subordinating conjunction JJ Adjective JJR Adjective, comparative JJS Adjective, superlative.
- The learning curve of Python is very fast and NLTK is written in Python so NLTK is also having very good learning kit. NLTK has incorporated most of the tasks like tokenization, stemming, Lemmatization, Punctuation, Character Count, and Word count. It is very elegant and easy to work with.
Import nltk from nltk.corpus import stateunion from nltk.tokenize import PunktSentenceTokenizer. Now, let's create our training and testing data: traintext = stateunion.raw('2005-GWBush.txt') sampletext = stateunion.raw('2006-GWBush.txt') One is a State of the Union address from 2005, and the other is from 2006 from past President George W. NLTK Tutorial in Python: What is Natural Language Toolkit? What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a process of manipulating or understanding the text or speech by any software or machine. An analogy is that humans interact and understand each other’s views and respond with the appropriate answer. The learning curve of Python is very fast and NLTK is written in Python so NLTK is also having very good learning kit. NLTK has incorporated most of the tasks like tokenization, stemming, Lemmatization, Punctuation, Character Count, and Word count. It is very elegant and easy to work with.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a process of manipulating or understanding the text or speech by any software or machine. An analogy is that humans interact and understand each other’s views and respond with the appropriate answer. In NLP, this interaction, understanding, and response are made by a computer instead of a human.
What is NLTK?
NLTK (Natural Language Toolkit) is a suite that contains libraries and programs for statistical language processing. It is one of the most powerful NLP libraries, which contains packages to make machines understand human language and reply to it with an appropriate response.
Here is what we cover in the Course
|Tutorial||Natural Language Processing Tutorial: What is NLP? Examples|
|Tutorial||How to Download & Install NLTK on Windows/Mac|
|Tutorial||NLTK Tokenize: Words and Sentences Tokenizer with Example|
|Tutorial||POS Tagging with NLTK and Chunking in NLP [EXAMPLES]|
|Tutorial||Stemming and Lemmatization with Python NLTK|
|Tutorial||WordNet with NLTK: Finding Synonyms for words in Python|
|Tutorial||Tagging Problems and Hidden Markov Model|
|Tutorial||Counting POS Tags, Frequency Distribution & Collocations in NLTK|
|Tutorial||Word Embedding Tutorial: word2vec using Gensim [EXAMPLE]|
|Tutorial||Seq2seq (Sequence to Sequence) Model with PyTorch|
What will you learn in this NLTK Tutorial for Beginners?
In this NLTK in Python tutorial, you will learn about introduction to NLTK, how to install NLTK, tokenize words, POS, Tokenization, Stemming, Lemmatization, Punctuation, Character count, word count, WordNet, Word Embedding, seq2seq model, etc.
Are there any prerequisites for this NLTK Tutorial?
Before learning this NLTK Python tutorial, it is advised for the learners to have the basic knowledge of Artificial Intelligence, Python Programming concepts, and English grammar.
Who is this NLTK Tutorial for?
This Python NLTK tutorial is for students who have an interest in learning Natural Language Processing. This guide will also help the working professionals to enhance their knowledge about NLP.
Why Learn Natural Language Toolkit?
Learning Natural Language Toolkit will help you add an extra skill and also enhance your knowledge of NLP. Learning NLTK library is also beneficial for professionals to enhance their careers in AI and Natural Language Processing with Python.
Various NLP Libraries
|NLTK||This is one of the most usable and mother of all NLP libraries.|
|spaCy||This is a completely optimized and highly accurate library widely used in deep learning|
|Stanford CoreNLP Python||For client-server-based architecture, this is a good library in NLTK. This is written in JAVA, but it provides modularity to use it in Python.|
|TextBlob||This is an NLP library which works in Pyhton2 and python3. This is used for processing textual data and provide mainly all type of operation in the form of API.|
|Gensim||Genism is a robust open source NLP library support in Python. This library is highly efficient and scalable.|
|Pattern||It is a light-weighted NLP module. This is generally used in Web-mining, crawling or such type of spidering task. p|
|Polyglot||For massive multilingual applications, Polyglot is best suitable NLP library. Feature extraction in the way on Identity and Entity.|
|PyNLPl||PyNLPI also was known as 'Pineapple' and supports Python. It provides a parser for many data formats like FoLiA/Giza/Moses/ARPA/Timbl/CQL.|
|Vocabulary||This library is best to get Semantic type information from the given text.|
In this NLTK tutorial in Python, we will only discuss one of the most popular NLP library NLTK.
Nltk Python Library
In this post, you will learn about getting started with natural language processing (NLP) with NLTK(Natural Language Toolkit), a platform to work with human languages using Python language. The post is titled hello world because it helps you get started with NLTK while also learning some important aspects of processing language. In this post, the following will be covered:
- Install / Set up NLTK
- Common NLTK commands for language processing operations
Install / Set up NLTK
This is what you need to do set up NLTK.
- Make sure you have Python latest version set up as NLTK requires Python version 3.5, 3.6, 3.7, or 3.8 to be set up. In Jupyter notebook, you could execute command such as !python –version to know the version.
- If you have got Anaconda set up, you can get started with executing command such as import nltk
- In case you don’t have Anaconda set up, you can execute the following command and get started. The command works well with Unix / Mac.
You could get started with practicing NLTK commands by downloading the book collection comprising of several books. Here is what you need to execute:
Executing above command will open up a utility where you could select book and download. Here is how it looks like:
Select the book and click download. Once the download is complete, you could execute the following command to load the book.
This is how it would look like by executing the above command.
Common NLTK Commands / Methods for Language Processing
Here are some of the common NLTK commands vis-a-vis their utility:
- nltk.word_tokenize(): Tokenize the sentence in words and punctuations. This includes the duplicate words and punctuations as well.
Here is how the output would look like:
- concordance() vs similar() vs common_contexts(): These are three methods which could be invoked on nltk.text.Text objects in order to find the following:
- concordance: Find the context in which a word / token occurred. The output is a set of different sentences where the word occurred.
- similar: Find the similar context in which the word occurred. Basically, find the sentences that consist same surrounding words (left and right).
- common_contexts: Find the pair of common surrounding words of the tokens passed to common_contexts method.
We will try and understand with one of the text (text7 – Wall Street Journal) loaded from nltk.book. In the example below, common_contexts output is to_the and to_their. This implies that to_the and to_their occurred around both the words, finance and improve. If the output of common_contexts would have been null / empty, the output of method similar would also have been null / empty.
Nltk Python 3
- FreqDist(): FreqDist() method counts the frequence of occurence of tokens (words or punctuations) and returns a FreqDist object representing a JSON object having key-value pair of key and number of occurrences. This method is present as part of nltk.probability package. You can use plot method on the instance of FreqDist to draw the plot.
Here is how the output plot would look like:
Nltk Python Documentation
- Printing tokens (words) satisfying some property such as token length: Here is a piece of code which can be used to print all words which meet certain criteria. For example, length of word > 5. Note the last statement in the code given below:
This is what will be printed
Nltk Python Install
Here is the sumary of what you learned in this post related to NLTK set up and some common methods:
- NLTK is a Python library to work with human languages such as English.
- NLTK provides several packages used for tokenizing, plots etc.
- Several useful methods such as concordance, similar, common_contexts can be used to find words having context, similar contexts.
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