Natural language processing (NLP) allows computers to analyze and understand written and spoken human (“natural”) language. This field of AI helps computers respond to commands, classify text, analyze data and translate between languages.

Understanding natural language processing

Human communication is complex – each language has its own:

• Grammar rules and exceptions
• Regional terminology and accents
• Use of sarcasm and inflections
• Figures of speech like metaphors and idioms
• Homonyms (here vs. hear)

These nuances make it difficult for non-human intelligence to accurately determine a sentence’s intended meaning. Using natural language processing, programmers can teach computers to accurately recognize and understand human communication.

The goal is for a computer to understand not just dictionary definitions, but also an author’s intent. (For instance, knowing sarcasm changes a sentence’s true meaning.)

How does natural language processing work?

NLP combines rule-based language models with statistics and computer science to teach computers to process language. While it gets complex quickly, NLP generally requires the following steps:

• The computer receives the text or audio input
• Built-in language models and machine learning convert natural language to programming language (speech-to-text)

• The NLP program “cleans” and processes text through steps like:

• Breaking the text into smaller pieces
• Identifying parts of speech

• Determining each word’s meaning in context

• The computer generates a response or completes a task (like categorizing data)
• The computer converts from programming to a visual or audio natural language (text-to-speech)

Different programs complete these tasks in various formations and with varying degrees of success. But thanks to advances in machine learning, modern programs often learn as they work, increasing speed and accuracy over time.

Benefits and challenges of natural language processing

Benefits of NLP

• Improves human-computer communication
• Increases speed and accuracy in automated data analysis
• Provides a foundation for personal assistants and chatbots
• Turns massive piles of data into advanced analytical insights 

Challenges in NLP

• Human speech is complex and doesn’t adhere to one set of rules, making it difficult for computers to understand exceptions, metaphors and context
• As grammar and vocabulary change, computers have to adapt to new rules
• NLP doesn’t easily pick up on sarcasm or emphasis
• Computers struggle to understand “thick” or unusual accents

Real-world uses for natural language processing

You’ve probably used an NLP-based application before, even if you didn’t know it. Common uses cases include:

• Personal assistants like Siri and Alexa
• Your car’s GPS
• Search engines like Google or Bing
• Translation services like Google Translate 
• Reducing email spam and phishing
• Customer service chatbots
• Experimental programs like ChatGPT
• Editing and plagiarism tools like Microsoft Word and Grammarly

Many businesses and research institutions also rely on NLP to process and analyze vast quantities of data stored in human language. Even social media sites have jumped on board, using NLP to help improve product development and marketing.

What this means for you

Natural language processing underscores much of the modern technology world. And as AI improves, it’s only getting better.

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