Natural Language Handling

Natural Language Handling

0%

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data. Our focus will encompass several core areas of natural language processing: tokenization, stemming, tagging and parsing. These NLTK features will form the backbone of our text processing and analysis tasks, making it an essential tool in our project for handling and extracting meaningful insights from language data.

List Comprehension

Regexp Tokenizer

Data Visualization

Course Content

Natural Language Handling

Natural Language Handling

Introduction Import NLTK Tokenization Stopwords List Comprehension Stemming Tagging Lemmatizer Regexp Tokenizer Data Visualization Word Cloud

Data Visualization

Now that we've covered the key features of the nltk package, let's move on to visualizing our data. We'll begin by calculating word frequencies and then displaying these frequencies using a bar plot.

Task

Calculate the frequency distribution of the tokenized words from your text.
Find the top 30 most common words in this distribution.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Now that we've covered the key features of the nltk package, let's move on to visualizing our data. We'll begin by calculating word frequencies and then displaying these frequencies using a bar plot.

Task

Calculate the frequency distribution of the tokenized words from your text.
Find the top 30 most common words in this distribution.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 10

AVAILABLE TO ULTIMATE ONLY

some-alt