This topic is a high-level introduction to natural language processing. It explains what natural language processing is, what the most common natural language processing applications are, and what the basic approaches to developing natural language processing applications are.
1. understanding natural language processing
People interact through natural language, which contains a lot of information. For example, the choice of different words, tone of voice, and context of sentences can reveal a person’s mood, intentions, and emotions.
In addition, text documents such as books, newspapers, and blogs are full of information that can be used for various tasks. For humans, it takes a lot of time to understand a document, extract useful information from it, and make decisions based on that information.
And this is where natural language processing comes into play.
Natural language processing is defined as “an area of artificial intelligence that enables computers to read, understand, and extract meaning from the natural language spoken by humans.”
With natural language processing, computers are not only able to understand natural language, but they can also respond to humans through natural language. NLP technology is used not only for human-computer interaction but also for processing large amounts of data from companies that would take months or even years to process manually.
For example, it might take a person weeks to manually extract information from a thousand-page book for a summary, but with natural language processing, a similar summary of a text can be done in minutes. Similarly, it is very costly to hire human readers to read through all the user reviews and create reports with users’ opinions about a particular product. However, with natural language processing, users’ opinions can be automatically extracted from the text, which enables companies to make organizational decisions.
2. Practical applications of natural language processing.
From Microsoft’s Cortona to Apple’s Siri, NLP provides support for a wide range of applications. This section provides a brief overview of some of the most common applications of NLP.
Sentiment Analysis
NLP is commonly used to perform textual sentiment analysis. Popular opinions in textual forms, such as tweets, blogs, and reviews about specific products or entities contain sentiment. Sentiment analysis refers to the identification of sentiment from these texts. Automatic detection of public sentiment about tweets can help companies decide how to improve their products and which ones to keep or discard.
Classification of both spam and non-spam.
Gmail and other email servers use NLP techniques to accurately distinguish between non-spam and spam. This is a classic application of text classification, where text documents are classified into one of the predefined categories, i.e., non-spam or spam.
Converting speech to text
NLP techniques are widely used to automatically convert speech to text in both directions. You no longer need to ask someone to write down what you say for you as you speak it. Google’s automatic speech recognition (https://bit.ly/2N6xYSt) is a classic example of speech-to-text conversion.
Human-Computer Interaction
Gone are the days when you had to press a keyboard key and click a mouse to issue commands to a computer; NLP has made it possible for humans and robots to interact through natural language. With the help of NLP, robots can not only understand human language but also respond to humans in natural language. Sophia (https://bit.ly/2YLprtx) is a classic example of a humanoid robot interacting with humans through natural language processing.
Augmented Virtual Assistants
As mentioned earlier, NLP techniques are also being used in the development of virtual assistants such as Amazon Alexa, Apple’s Siri, and Microsoft’s Cortona. These virtual personal assistants rely on NLP methods to understand human requests and respond in natural language.
Text generation
Advanced NLP techniques based on Deep Learning are now being used for text generation. In recent years, text generation techniques have been used to generate poetry based on (Game of Thrones) scripts. Text generation techniques are still in the early stages.
3. Roadmap for learning NLP.
Mastering NLP takes time and effort. You can’t claim to be an NLP expert just because you read two blogs. This section describes how you can learn NLP step by step.
To become a good NLP expert, you need to follow the steps below to learn.
3.1 Understand the full meaning of NLP
Before you can start developing an NLP application, you need to know what you are actually doing. You should understand the full meaning of NLP, why it is useful, and some of the major NLP applications. The first chapter of this book is about laying a foundation for natural language processing theory.
3.2 Learning a Programming Language
There is no getting around the fact that if you want to become an NLP expert, you need to learn some programming. You need to learn programming in order to develop NLP applications. Although you can use any programming language to program natural language applications, I would recommend learning the Python programming language. Python is one of the most widely used libraries for NLP, with a myriad of basic and advanced NLP libraries. In addition, many NLP applications are based on machine learning and deep learning. And Python is a language that provides easy-to-use Deep Learning and Machine Learning libraries. In short, learn Python. Chapter 2 provides a crash course in getting started with Python for beginners.
3.3 Start with basic tasks
Start with very basic NLP applications. Beginners are advised not to start developing NLP applications right away, but to familiarize themselves with the most common and basic NLP tasks first. For example, learn how to perform word deactivation, how to divide sentences into words and paragraphs into sentences, know how to find part-of-speech tags for words in the text, etc., etc. In addition, mastering text cleanup and manipulation techniques is a must. Finally, you need to master how to import data from various sources into your application and how to crawl data from a website to import it into your application. The basic NLP tasks are explained in detail in Chapters 4, 5, and 6.
3.4 Understanding how to represent text statistically
As mentioned earlier, most NLP techniques incorporate the concepts of both Deep Learning and Machine Learning, both of which are statistical in nature. In order to implement these techniques in NLP, text must be represented statistically. There are several methods that can be used to represent text statistically; see Chapter 7 for more information.
3.5 Understanding Machine Learning and Deep Learning
Once all the basic NLP concepts have been learned, machine learning and deep learning concepts, particularly supervised machine learning algorithms, must then be learned. For deep learning algorithms, one should focus on understanding the basic operating principles of densely connected neural networks, recurrent neural networks (especially LSTM), and convolutional neural networks. These concepts will be explained in Chapter 3.
3.6 Developing Advanced NLP Applications
Once you are familiar with basic NLP tasks and have a basic understanding of deep learning and machine learning, you are ready to develop advanced NLP applications. For NLP applications, it is recommended to first develop machine learning applications (e.g., text classification) using machine learning algorithms such as Logistic Regression, Random Forest, etc. Once one is able to develop NLP applications using machine learning in a familiar way, one can move on to advanced applications using various neural networks based on Deep Learning. Part 2 of this book is devoted to the development of Deep Learning and Machine Learning-based NLP applications.
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