Introduction

In this tutorial, our goal is to provide an introduction to Natural Language Processing (NLP) and its various applications. By the end of this tutorial, you will understand the basic concepts of NLP, how it's used to enable machines to understand human language, and you will have hands-on experience with practical application examples.

Prerequisites:
- Basic understanding of Python programming.
- Familiarity with Machine Learning concepts will be beneficial but not essential.

Step-by-Step Guide

What is Natural Language Processing?

Natural Language Processing (NLP) is a branch of Artificial Intelligence that deals with the interaction between computers and humans using natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human language in a valuable way.

NLP Applications

There are several practical applications of NLP like sentiment analysis, text summarization, speech recognition, etc. In this tutorial, we will focus on a simple application - Text Classification.

Text Classification

Text classification is a technique for assigning tags or categories to text according to its content. It's one of the fundamental tasks in Natural Language Processing.

Code Examples

We will use Python's nltk library for our NLP tasks. Make sure you have it installed in your environment (pip install nltk).

# Importing required libraries
import nltk
from nltk.corpus import brown

# Download the brown corpus (if not already downloaded)
nltk.download('brown')

# Get the categories in the brown corpus
categories = brown.categories()
print(categories)

The above code loads the Brown Corpus (a collection of text samples from a wide range of sources, with a total of over a million words) and prints its categories.

Summary

In this tutorial, we introduced Natural Language Processing, its applications, and performed a simple text classification task. Your next steps should be exploring more complex NLP tasks and techniques like Named Entity Recognition, Sentiment Analysis, etc. You can find more resources on the NLTK official documentation (https://www.nltk.org/).

Practice Exercises

1. Exercise 1: Load any other corpus from nltk and print its categories.

Solution:

# Importing required libraries
import nltk
from nltk.corpus import reuters

# Download the reuters corpus (if not already downloaded)
nltk.download('reuters')

# Get the categories in the reuters corpus
categories = reuters.categories()
print(categories)

1. Exercise 2: Load the 'inaugural' corpus, print its file ids, and print the raw text of any one file id.

Solution:

# Importing required libraries
import nltk
from nltk.corpus import inaugural

# Download the inaugural corpus (if not already downloaded)
nltk.download('inaugural')

# Get the file ids in the inaugural corpus
file_ids = inaugural.fileids()
print(file_ids)

# Print the raw text of the first file id
raw_text = inaugural.raw(file_ids[0])
print(raw_text)

For further practice, try to work with different corpora and perform more complex tasks like tokenization, lemmatization, etc.