What Is Natural Language Processing (NLP), and How Does It Work?

natural language processing

What Is Natural Language Processing (NLP), and How Does It Work?

Key Points

  • NLP is a subfield of linguistics, computer science, and AI that enables computers to understand human language.
  • NLP has two main components: natural language generation (NLG) and natural language understanding (NLU).
  • NLP has various applications, including sentiment analysis, chatbots, spam and phishing detection, creation of text summaries, and translation.
  • NLP has been around for over seventy years, but has recently started enjoying greater usage due to the emergence of AI and big data.
  • Examples of NLP in everyday life include search engine results, automated phone calls, predictive texts, and online consumer computing platforms assistants like Alexa.

Natural language processing (NLP) is a subfield of linguistics, computer science, and AI, and it uses various models to allow a computer to understand human language. By using NLP, computers are equipped with the ability to comprehend natural spoken and written human language.

In the age of big data and the emergence of AI in diverse forms, you might assume that NLP is new. However, in its various forms, NLP has been around for over seventy years. What has happened recently is that NLP has started enjoying greater usage. This trend can only increase in the years ahead.

Looking at the past, the advancements that have taken place in NLP seem incredible. Namely, computers today are able to communicate with humans in their languages, hear and interpret speech, and then decide which information is important or trivial.

What Is Natural Language Processing: Complete Explanation

NLP aims to make computers understand natural human languages the same way humans do. By using AI, NLP makes computers understand both written and spoken language. After data collection, NLP uses AI to process it to help the computer understand it.

NLP simulates the human way of collecting and processing information. Humans gather data by listening, watching, and reading.

In the same way, computers are equipped with programs that read and microphones that gather audio data. And just as the human brain processes information, computers have programs that process data.

Natural Language Processing: An Exact Definition

Natural language processing is the use of machine learning technology to analyze and synthesize natural speech and language.

The History of Natural Language Processing

NLP began after WWII. Those who developed it were principally interested in creating machines that could automatically translate from one language to another.

what are Large Language Models
Neural networks, machine learning, and NLP are terms related to the study of language models.

Famous linguists such as Noam Chomsky took NLP further in the mid-1950s. From their research, it became evident that even though computers could perform some translation tasks, they were limited by the complexity of human languages.

Today, NLP focuses mostly on the extraction of information from the vast reservoir that is the internet. Since there’s so much information available, processing it is humanly impossible. Business owners have so much data that needs to be processed that the only way to make sense of such massive quantities of data is through NLP.

How Does Natural Language Processing Work?

NLP has two main components: natural language generation (NLG) and natural language understanding (NLU). Through NLG, data is converted into meaningful sentences and phrases that resemble natural language. The NLG process works through three stages, which are:

  1. Text planning: This refers to the retrieval of required data.
  2. Sentence planning: The formation of sentences and phrases and the creation of tone.
  3. Text realization: The development of sentence structure.

Natural language understanding (NLU) helps computers to understand human language from the data input. NLU analyzes the different aspects of human language and converts the input data into valid natural language.


While NLU is supposed to help machines comprehend human language, the process is made complicated by the ambiguities inherent in all human languages. A computer has to be trained to understand the exact meaning of a word or phrase. Difficulties arise where:

  • A word has several meanings: Words like bark, match, park, chair, and left, also known as homonyms, have multiple meanings. “Bark” could refer to the bark of a tree or the barking of a dog. “Park” could refer to parking a car or a recreation ground, and the word “left” could mean depart, the left side, etc.
  • A phrase has several meanings: Phrases that have more than one meaning are called double-entendre phrases. The phrase “children make good snacks,” for example, could mean either that children perform the act of preparing snacks or that someone or something is using children as snacks.
  • A phrase or word refers to two or more different things: This is known as referential ambiguity, as in the sentence, “George slapped Tom and then he started screaming.” Here we can’t tell whether it’s George or Tom who started screaming.

To overcome the ambiguities inherent in human language, NLP uses several techniques. Some of the techniques that can remove ambiguities and make the intended meaning of a word or phrase clear include word sense disambiguation, parts of speech tagger, and hidden Markov model (HMM) tagger.

What Are the Applications of Natural Language Processing?

In the age of big data, the amount of data that organizations have access to is so huge that there would be no way to process it without the use of machines. But, thanks to NLP, businesses today have countless applications that are run using AI. Some of the most crucial ones include the following.

Sentiment Analysis

No modern business can ignore the mentions, likes, shares, and other activities that are rife on social media. A business wants to know how its products or services are viewed by its customers.

From social media, a business gets priceless insights into what it needs to do to improve products and services. Through sentiment analysis, NLP is helping with product design and the preparation of marketing campaigns.


ai chatbot
A chatbot is a conversational agent that uses NLP to understand and respond to human language inputs.

Some businesses have so many customer inquiries to handle that they would need an army of customer care reps to handle queries. Chatbots save the company time and resources by answering questions and providing basic information about an organization’s services.

By using chatbots, the company can deploy customer care reps to handle only the complicated queries that demand real human intervention.

Spam and Phishing Detection

NLP classifies texts and detects mail that is likely to be spam. Machines have been trained to instantly classify email that uses inappropriate grammar, huge monetary promises, threats, and intimidation as most likely to be spam.

Other indicators of mail that could be spam include unsolicited and irrelevant emails that are sent from dubious companies and individuals. By filtering spam, NLP helps individuals and organizations avoid the risks and irritation that are inevitable from such emails.

Creation of Text Summaries

With so much data available on the internet, there’s hardly an organization or individual that has enough time to read the vast volumes of it.

Organizations such as research institutions usually need to handle voluminous data that might prove impossible to read. NLP summarizes such data and provides synopses that can be used to create indices.


While NLP has come a long way in making translations from different languages possible, there is still a lot of ground to cover when you remember that there are over 6,500 global languages. But NLP has made it possible to effectively translate some of the widely spoken languages.

With NLP language translation, you’re not just getting the equivalent words of two languages. On the contrary, for the translation to be considered effective, it has to capture the tone and exact meaning of a word or phrase.

The machine learning algorithms used in NLP are more and more accurate and we are looking at a future where we can trust them to deliver perfect translations.

Examples of Natural Language Processing In the Real World

Natural language processing is used daily by billions of people around the world. The fact is that we are so used to digital devices that we hardly realize that natural language processing is part and parcel of daily life. Here are a few real-world examples of NLP applications.

Search Engine Results

To get to the article you’re reading now, you probably typed some keywords in the Google search engine. You’ll notice that the moment you start typing when searching for something, the search engine can already predict what you could be looking for and gives you options.

By using NLP, a search engine provides results guided by your search history and similar searches conducted by others. Moreover, the search engine can predict your intent and provide a list of possible results.

Automated Phone Calls

Rather than employ humans to receive and answer calls, many organizations today make use of machines that use NLP to answer queries or to direct customers to human customer care reps. NLP is not only used to provide this service but, also enables the machine to speak in a voice that closely resembles the human voice.

Predictive Texts

When typing on your smartphone, you’ll have noticed that search engines help by completing words, correcting spelling, and suggesting alternative words. This is NLP at work and it saves time.

Through predictive text, NLP can already tell what you might be attempting to say based on your writing history and provide the words you could be looking for.

Online Consumer Computing Platforms Assistants

If you’ve shopped on Amazon, you must be aware of the services offered by Alexa. Today, we’re getting to the stage where we are used to asking smart online assistants, such as Alexa or Apple’s Siri, various questions.

Final Thoughts

In the space of a few years, natural language processing has completely revolutionized the way we interact with machines. By using NLP, individuals and organizations have delegated routine tasks to machines.

Since computers are capable of analyzing unimaginably vast amounts of data with great accuracy, we can only expect them to be used even more widely in the future. While machines can never replace human beings, their use in our lives is clear and the interaction between Man and machine is irreversible.

Frequently Asked Questions

What are the main applications of NLP?

NLP finds applications in various domains, including but not limited to:

  • Sentiment analysis and opinion mining
  • Text classification and categorization
  • Information retrieval and question-answering systems
  • Machine translation and language generation
  • Speech recognition and synthesis
  • Named entity recognition and entity extraction
  • Text summarization and document understanding

What are examples of NLP in everyday life?

Examples of NLP in everyday life include virtual assistants like Siri and Alexa, spam email filters, autocorrect and predictive text on smartphones, language translation services like Google Translate, chatbots, voice-controlled devices, and sentiment analysis of customer reviews.

What are the key components of NLP systems?

NLP systems typically consist of several components, such as text preprocessing (tokenization, stemming, etc.), part-of-speech tagging, syntactic parsing, semantic analysis, named entity recognition, co-reference resolution, sentiment analysis, machine learning models, and natural language generation.

What are the challenges in implementing NLP?

Implementing NLP poses challenges such as dealing with language ambiguity, understanding context and sarcasm, handling different languages and dialects, managing large and diverse datasets, and training models that generalize well across different domains.

How does NLP handle different languages?

NLP techniques can be adapted to different languages by building language-specific models and resources. This involves training models on large corpora of text in the target language, developing language-specific rules and patterns, and addressing linguistic nuances and challenges specific to that language.

Are there any ethical concerns or considerations related to NLP?

Yes, there are ethical concerns related to NLP, such as privacy issues in processing personal data, bias and fairness in language models, the potential misuse of NLP for malicious purposes, and the impact of automated systems on employment and job displacement. Ethical guidelines and responsible use of NLP are essential for addressing these concerns.

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