2 Your first NLP example

In this chapter, you will learn how to implement your own NLP application from scratch. In doing so, you will also learn how to structure a typical NLP pipeline and how to apply a simple machine learning algorithm to solve your task. The particular application you will implement is spam filtering. We overviewed it in Chapter 1 as one of the classic tasks on the intersection of NLP and machine learning.

2.1       Introducing NLP in practice: spam filtering

In this book, you use the spam filtering as your first practical NLP application as it is an example of a very widely spread family of tasks – text classification. Text classification comprises a number of applications that we discuss in this book, for example user profiling (Chapter 5), sentiment analysis (Chapter 6) and topic labeling (Chapter 8), so this chapter will give you a good start for the rest of the book. First, let’s see what exactly classification addresses.

2.2       Understanding the task

2.3        Implementing your own spam filter

2.3.1   Step 1: Define the data and classes

2.3.2   Step 2: Split the text into words

2.3.3   Step 3: Extract and normalize the features

2.3.4   Step 4: Train the classifier

2.3.5   Step 5: Evaluate your classifier

2.4       Deploying your spam filter in practice

2.5       Summary