What Is Natural Language Processing?

Recent technological advances are reshaping how humans interact with machines. One of the most popular and revolutionary inventions is ChatGPT, an artificial intelligence (AI) tool developed by the OpenAI laboratory. ChatGPT uses natural language processing (NLP) to converse with users and perform various tasks, from writing articles to suggesting baby names.¹ But what is natural language processing and why is it important?

Natural language processing helps computers understand, analyze, and generate human language. This technology allows humans to communicate with machines more intuitively without using programming languages. Because ChatGPT and other NLP tools are so accessible, they have many practical applications.² This article explores how NLP works, its relationship to AI, and popular uses of this novel technology.

What Is NLP?

Natural language processing is a field of AI that allows humans and computers to communicate using everyday human language. Traditionally, humans have used coding languages like HTML and Java to interact with computers. However, NLP simplifies this process by enabling computers to understand, process, and generate natural human language.³

NLP combines computational linguistics and computer science to help computers interpret speech and written language.³ These AI-powered systems imitate how humans learn and understand language by following a relatively straightforward process:

Tokenization

First, scientists train an NLP program to break down complex language into smaller units called tokens. These tokens are the building blocks of human language and include individual words, names, and punctuation.² Say you ask an NLP system, such as a chatbot, “How do I cook macaroni?” The program will reduce the sentence to these tokens: “how” “do” “I” “cook” “macaroni” “?”

Lexical Analysis

The NLP draws on linguistic principles to understand the lexical meaning of each token. It assigns every token a part-of-speech tag based on the context of the sentence.² For instance, the NLP program will interpret “cook” as a verb and “macaroni” as a noun.

Semantic Processing

Based on the lexical analysis, the system uses semantic processing to identify the most relevant tokens and extrapolate the meaning of the entire sentence.² In this example, the word “cook” would signal to the NLP that the user is most likely referring to macaroni and cheese, not the eighteenth-century fashion trend.⁴

Natural Language Generation

Finally, the application responds to the user’s input in fluent human language. The program will select the appropriate content, organize it based on grammatical rules, and generate text or speech.² In this hypothetical scenario, the NLP application could respond to the user’s inquiry by writing a recipe with step-by-step instructions for cooking macaroni and cheese.

Advanced Processes

Scientists can expand this basic process to teach the NLP system to perform more complex tasks, such as:³

• Recognizing emotions and opinions in text or speech
• Flagging duplicate content in documents
• Generating an index
• Summarizing documents
• Translating documents from one language to another
• Structuring large amounts of data into a coherent document
• Extracting specific content from books and websites

What is natural language processing in AI?

Artificial intelligence is an advanced computer program that simulates how humans think, learn, and make decisions. AI learns to identify patterns and trends in datasets and continuously adapts its performance in response.⁵

NLP is a branch of AI that focuses on analyzing and mimicking how humans speak and write. ChatGPT and many other NLP applications are large language models (LLM), which are AI-powered systems used to interpret human language. Researchers typically use large pieces of text, such as Wikipedia articles, to teach LLMs rules of grammar, common phrases, and other elements of language.⁶

What is natural language processing used for?

NLP may seem like a recent invention, but this technology has already infiltrated many parts of our daily and professional lives. Consider three popular uses for NLP.

Virtual Assistants

Virtual assistants are applications that use NLP to understand human speech and obey programmed commands. The most popular virtual assistants include Amazon’s Alexa, Apple’s Siri, and Google Assistant. These accessible applications are typically pre-installed on computers, smartphones, and tablets.⁷

Users activate the virtual assistants by saying their names. They can ask questions or give commands in conversational language, and the programs use NLP to translate their speech and perform actions. For instance, you can ask Siri to tell you the weather forecast or set a timer while you cook dinner.⁷

Content Generation

Platforms like ChatGPT use NLP to process vast amounts of information and generate relevant content in seconds. These tools can produce a broad range of content, such as blog posts, press releases, and obituaries.⁸

Several prominent news publications, for example, have used NLP to write self-reflective articles about AI. In 2020, The Guardian published an article by OpenAI’s language generator GPT-3. Titled “A robot wrote this entire article. Are you scared yet, human?,” the article uses NLP to explain why humans shouldn’t fear AI.⁹

Generative NLP has many advantages, like helping creators develop new ideas and reducing writing and editing time. However, ChatGPT and other generative AI tools can also promote misinformation and may plagiarize existing sources, which has raised some ethical concerns.⁸

Document Summarization

Businesses can use NLP tools such as MeaningCloud and ML Analyzer to summarize spoken and written language. For example, these tools can identify key concepts and words in long documents or audio recordings and write a summary. This application is especially useful for professionals working with dense legal and technical documents.¹⁰

Harness the Power of NLP for Your Career Success

Natural language processing is one of the most powerful tools for business analytics. Professionals can use this ground-breaking technology to analyze documents, understand how consumers respond to products, and much more.

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