What is natural language processing (NLP)?

Today, we are familiar with numerous data sources coming from messages, social media, emails, audios, reviews… and there is a growing need for systems to “understand” what humans say or write. This is where Natural Language Processing (NLP) comes in.

In this article, we will explore what NLP is, what it’s used for, its challenges, and how it can play a key role in products or services for companies.

NLP: a simple overview

What is NLP?

“NLP” stands for Natural Language Processing. It refers to the field or set of techniques that enable machines to interpret, analyze, process, and generate human language, either written or spoken, in a way that makes sense to us.

In other words: it’s the ability for a computer to understand what you say or write, in your language, with your expressions, your ambiguities, your nuances. It’s not magic; it’s a combination of linguistics, statistics, machine learning, and a lot of engineering.

According to IBM, Natural Language Processing (NLP) is a “subfield of computer science and artificial intelligence that uses machine learning to enable computers to understand and communicate with human language, combining linguistic rules, statistical modeling, and deep learning techniques.”

Therefore, when we talk about NLP, we are talking about the ability to bring machines closer to natural human language.

Why does Natural Language Processing matter?

Here are a few reasons why Natural Language Processing (NLP) is gaining traction in many industries:

• Scalability in text data analysis: companies accumulate massive amounts of opinions, emails, chat logs, reports, etc. It’s impossible to process it all manually. NLP allows for the automation of extracting insights.
  
• Improved customer experience: with chatbots, virtual assistants, or automated response systems, you can offer instant, personalized customer service available 24/7.
  
• Sentiment and reputation analysis: knowing whether users are happy or not, what words they use to describe your brand, and detecting emerging trends.
  
• Automation of repetitive tasks: from writing automatic summaries, translating texts, to correcting grammar or generating basic content.
  
• Fraud detection, compliance, and contract mining: in sectors like finance or legal, NLP helps detect clauses, anomalies, risks, without someone needing to read thousands of pages.

For example, Zendesk claims that NLP enables smarter chatbots that step in just when needed, identify user intent, and escalate complex cases.

As you can see, NLP is not something distant; it’s already very present, even if we don’t always notice it.

Some common NLP examples you might know

To make the concept clearer, let’s look at some common applications:

• Email filters (Spam / Not Spam / Promotions): the system analyzes the message’s content and decides where to place it.
  
• Virtual assistants: they convert speech to text, interpret intent, and respond coherently.
  
• Automatic translation: tools that use advanced NLP techniques to provide translations in various languages.
  
• Automatic summarization: turning long texts into concise summaries that capture the essentials.
  
• Social media sentiment analysis: detecting if a comment is positive, negative, or neutral, and identifying trends in opinions.
  
• Ticket or email classification in support systems: automatically assigning topics or urgency levels.
  

These examples show that NLP is behind many of the small conveniences of digital life.

How does natural language processing work?

Although we won’t dive into formulas, it’s helpful to understand the basic stages or components of NLP to see where the complexity lies:

Tokenization and cleaning

Words, punctuation, and sentences are separated. Unnecessary symbols are removed, and the text is normalized (lowercase, removing accents, etc.).

Tagging and syntactic analysis

Each word is labeled with its role (noun, verb, adjective), and the sentence structure is analyzed. This step helps understand how words relate to each other.

Entity and relationship extraction

Recognizing proper names, dates, places, organizations, and relationships between them. For example, in “Apple bought Beats,” recognizing “Apple” as a company, “Beats” as a brand, and the relationship between them.

Sentiment/Intent analysis

Determining whether a text expresses something positive, negative, or neutral, or identifying the intent behind it (“want to buy,” “ask,” “complain”).

Language generation

Not only understanding but also generating coherent sentences, like responding in a chatbot, summarizing, or writing automatic texts.

What makes NLP so challenging is that human language is full of ambiguities, idioms, contexts, and irony… and each language has its own particularities.

A key aspect is representing language in vectors, known as word embeddings, which allow words to have “similar vectors” if they appear in similar contexts.

Moreover, many modern techniques use deep learning models based on transformers (like BERT, GPT) to capture long-range context. Studies on the evolution of NLP highlight that models like GPT-5 or BERT have made significant advances in linguistic understanding and generation.

NLP challenges and limitations

Not everything is perfect. Some of the challenges NLP faces include:

• Linguistic ambiguity: the same sentence can be interpreted in many ways depending on the context.
  
• Figurative language, irony, sarcasm: hard for a machine to pick up on these nuances.
  
• Low-resource languages: languages with limited available data have fewer tools. For example, Spanish still has many gaps.
  
• Bias in training data: if models are trained on biased data, they will reproduce those biases.
  
• Computational cost: large models require significant resources (memory, processing power).
  
• Interpretability: it’s not always clear why a model made a specific decision.
  

That’s why NLP tends to perform well in specific, well-defined tasks (classification, extraction) and less well when dealing with more general or free-form content.

NLP in the educational environment

At Founderz, we offer advanced AI training, and as part of our programs, we include NLP modules in the Online Certificate Program in AI and Innovation so that students not only understand how machines “understand” human language in various fields but can also apply it practically.

Thus, Natural Language Processing (NLP) isn’t just an abstract concept; it’s a practical tool for creating real value.

Natural Language Processing (NLP) is the gateway that allows machines to “read” and “write” like us, dealing with the nuances of human language. While it faces challenges like ambiguity, cost, and biases, its applications are already present in services we use every day.

If you’re interested in exploring how Natural Language Processing (NLP) can transform your project or business, contact us. We’d be happy to help you get started practically and with visible results.

This post is also available in: Español