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The Natural Language Processing Workshop
The Natural Language Processing Workshop

The Natural Language Processing Workshop: Confidently design and build your own NLP projects with this easy-to-understand practical guide

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Profile Icon Rohan Chopra Profile Icon Muzaffar Bashir Shah Profile Icon Nipun Sadvilkar Profile Icon Aniruddha M. Godbole Profile Icon Dwight Gunning Profile Icon Sohom Ghosh +2 more Show less
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The Natural Language Processing Workshop

2. Feature Extraction Methods

Overview

In this chapter, you will be able to categorize data based on its content and structure. You will be able to describe preprocessing steps in detail and implement them to clean up text data. You will learn about feature engineering and calculate the similarity between texts. Once you understand these concepts, you will be able to use word clouds and some other techniques to visualize text.

Introduction

In the previous chapter, we learned about the concepts of Natural Language Processing (NLP) and text analytics. We also took a quick look at various preprocessing steps. In this chapter, we will learn how to make text understandable to machine learning algorithms.

As we know, to use a machine learning algorithm on textual data, we need a numerical or vector representation of text data since most of these algorithms are unable to work directly with plain text or strings. But before converting the text data into numerical form, we will need to pass it through some preprocessing steps such as tokenization, stemming, lemmatization, and stop-word removal.

So, in this chapter, we will learn a little bit more about these preprocessing steps and how to extract features from the preprocessed text and convert them into vectors. We will also explore two popular methods for feature extraction (Bag of Words and Term Frequency-Inverse Document Frequency), as well as various methods...

Types of Data

To deal with data effectively, we need to understand the various forms in which it exists. First, let's explore the types of data that exist. There are two main ways to categorize data (by structure and by content), as explained in the upcoming sections.

Categorizing Data Based on Structure

Data can be divided on the basis of structure into three categories, namely, structured, semi-structured, and unstructured data, as shown in the following diagram:

Figure 2.1: Categorization based on content

These three categories are as follows:

  • Structured data: This is the most organized form of data. It is represented in tabular formats such as Excel files and Comma-Separated Value (CSV) files. The following image shows what structured data usually looks like:

Figure 2.2: Structured data

The preceding table contains information about five people, with each row representing a person and each column representing one of their attributes...

Cleaning Text Data

The text data that we are going to discuss here is unstructured text data, which consists of written sentences. Most of the time, this text data cannot be used as it is for analysis because it contains some noisy elements, that is, elements that do not really contribute much to the meaning of the sentence at all. These noisy elements need to be removed because they do not contribute to the meaning and semantics of the text. If they're not removed, they can not only waste system memory and processing time, but also negatively impact the accuracy of the results. Data cleaning is the art of extracting meaningful portions from data by eliminating unnecessary details. Consider the sentence, "He tweeted, 'Live coverage of General Elections available at this.tv/show/ge2019. _/\_ Please tune in :) '. "

In this example, to perform NLP tasks on the sentence, we will need to remove the emojis, punctuation, and stop words, and then change the words...

Feature Extraction from Texts

As we already know, machine learning algorithms do not understand textual data directly. We need to represent the text data in numerical form or vectors. To convert each textual sentence into a vector, we need to represent it as a set of features. This set of features should uniquely represent the text, though, individually, some of the features may be common across many textual sentences. Features can be classified into two different categories:

  • General features: These features are statistical calculations and do not depend on the content of the text. Some examples of general features could be the number of tokens in the text, the number of characters in the text, and so on.
  • Specific features: These features are dependent on the inherent meaning of the text and represent the semantics of the text. For example, the frequency of unique words in the text is a specific feature.

Let's explore these in detail.

Extracting General Features...

Finding Text Similarity – Application of Feature Extraction

So far in this chapter, we have learned how to generate vectors from text. These vectors are then fed to machine learning algorithms to perform various tasks. Other than using them in machine learning applications, we can also perform simple NLP tasks using these vectors. Finding the string similarity is one of them. This is a technique in which we find the similarity between two strings by converting them into vectors. The technique is mainly used in full-text searching.

There are different techniques for finding the similarity between two strings or texts. They are explained one by one here:

  • Cosine similarity: The cosine similarity is a technique to find the similarity between the two vectors by calculating the cosine of the angle between them. As we know, the cosine of a zero-degree angle is 1 (meaning the cosine similarity of two identical vectors is 1), while the cosine of 180 degrees is -1 (meaning...

Summary

In this chapter, you have learned about various types of data and ways to deal with unstructured text data. Text data is usually extremely noisy and needs to be cleaned and preprocessed, which mainly consists of tokenization, stemming, lemmatization, and stop-word removal. After preprocessing, features are extracted from texts using various methods, such as BoW and TFIDF. These methods convert unstructured text data into structured numeric data. New features are created from existing features using a technique called feature engineering. In the last part of this chapter, we explored various ways of visualizing text data, such as word clouds.

In the next chapter, you will learn how to develop machine learning models to classify texts using the feature extraction methods you have learned about in this chapter. Moreover, different sampling techniques and model evaluation parameters will be introduced.

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Key benefits

  • Get familiar with key natural language processing (NLP) concepts and terminology
  • Explore the functionalities and features of popular NLP tools
  • Learn how to use Python programming and third-party libraries to perform NLP tasks

Description

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.

Who is this book for?

This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.

What you will learn

  • Obtain, verify, clean and transform text data into a correct format for use
  • Use methods such as tokenization and stemming for text extraction
  • Develop a classifier to classify comments in Wikipedia articles
  • Collect data from open websites with the help of web scraping
  • Train a model to detect topics in a set of documents using topic modeling
  • Discover techniques to represent text as word and document vectors
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Table of Contents

8 Chapters
1. Introduction to Natural Language Processing Chevron down icon Chevron up icon
2. Feature Extraction Methods Chevron down icon Chevron up icon
3. Developing a Text Classifier Chevron down icon Chevron up icon
4. Collecting Text Data with Web Scraping and APIs Chevron down icon Chevron up icon
5. Topic Modeling Chevron down icon Chevron up icon
6. Vector Representation Chevron down icon Chevron up icon
7. Text Generation and Summarization Chevron down icon Chevron up icon
8. Sentiment Analysis Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(5 Ratings)
5 star 60%
4 star 40%
3 star 0%
2 star 0%
1 star 0%
N/A Dec 20, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a good book for beginners. The examples are clear. Overall, it is a very good book.
Feefo Verified review Feefo
Darpan Feb 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
All NLP practitioners are having trouble finding natural language processing books to start with. Particularly the lack and insufficient understanding of each topic required base to learn. BERT, NLTK, Semantic Efficacy, Segment Embedding, Contextual Embedding all of these are very important and important topics to learn in productive Environment.This book is very good to start with basics. All topics are covered in a particular manner. All functionality is covered with detailed understanding and and hands on method. With this book users can read and learn very quickly because of the easy language Good thing about this book is that its practical implementation of each topic will help us to understand details of implementation has been done in algorithm.Overall it is a very awesome book for nlp.
Amazon Verified review Amazon
William Kpabitey Kwabla Oct 01, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Explanation Of ConceptThe authors took their time to really explain the concepts for everyone to really understand them. I have a preference of understanding concepts than memorizing them and the authors did an amazing job explaining conceptsClarity of CodeWith the amazing explanations of concepts, they did a great job with the implementation of the concepts in python.ProjectsI like project based learning which is what the authors used to teach and implement the concepts. Aside from that, they added many exercises for each chapter to help solidify what you learn in every chapter.
Amazon Verified review Amazon
Nitin Kishore Jan 25, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I've been looking for a good book to refresh my basic concepts since finishing grad school. This books is an extensive package and doesn't just address NLP in terms of recent popular sub fields like chat bots or Bert but rather goes into multiple levels of linguistic analysis like morphology, discourse, syntax, semantics etc. and provides a more well rounded introduction to all sub fields. It's perfect as an intro. This book is easy to consume and filled with python snippets that help you understand what's happening through code.NLTK to spell check your data before lemmatizing was a good tip that is usually missed by many people even in the industry. They do go into using textblob as well. I wish they could have included some spacy related content and went into some use cases or SOTA for each topic. That would help people currently working in the industry as well. Perhaps they would cover that in another book.Exercises, examples and pipelines are very practical and go over standard text processing practices and how to do them. Some of these are generalized and useful in other non NLP related projects as well. The theory is brief, to the point, easy to follow even without any examples and it is not math heavy which is usually what intimidates beginners or non STEM students and professionals from engaging in reading. This is the perfect book for them to start their learning and explore further.Towards the end they do introduce Bert in context of text summarization and explain word vectors and embedding which is crucial if you want to delve into deep learning models. Would love to see more advanced content as well that covers Deep NLP
Amazon Verified review Amazon
Hakuna Matata Feb 08, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Very few beginner books in NLP I have seen with details on word sense disambiguation, sentence boundary detection, and detailed chapter in feature extraction and data preparation. I also loved the chapters on web scrapping and dealing with semi structured data - very important skill for a data scientist in general.Love the code snippets and the ability to test those snippets in mybinder. I didn't have to worry about setting up my own Jupyter setup to get going. This is a huge barrier for many developers who want to just focus on learning the concepts instead of getting bogged down with environment setup.
Amazon Verified review Amazon
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