Natural Language Processing: What Is It and How It Works

What is Natural Language Processing?

Natural Language Processing (NLP) refers to an area between linguistics and computer science within artificial intelligence, concerned with how computers and humans communicate via language. It allows us to take human language and use it in ways that make sense for a computer.

NLP Basics

To better understand NLP, it helps to break it down into its core components:

• Word structure and language rules: Syntax such as nouns, verbs, prefixes, and suffixes
• Meaning: Semantics refer to the meaning of words and sentences and the relationship between these
• Context: Pragmatics looks at how context and setting influence the way a message is interpreted
• Previous conversation: Discourse examines how previous sentences impact the interpretation of the next sentence
• Spoken language conversion technology: Speech recognition involves converting spoken words to text

Applications of NLP

From chatbots to email filtering, NLP is rapidly transforming the way we do business. Here are a few of the applications of NLP that are already making a big impact:

NLP History

The history of natural language processing (NLP) dates back more than half a century. The field has evolved dramatically over the years.

The earliest natural language processing systems were based on
**handcrafted rules**, which constrained their capabilities and limited their effectiveness. However, the advent of large-scale, high-quality datasets and advances in machine learning algorithms have triggered a shift in NLP systems from traditional rule-based approaches to data-driven architectures architectures. By using large amounts of data to train and validate machine learning algorithms, researchers and scientists can create systems that are more robust and capable of producing higher absolute levels of performance
*Doran, Charles, Yanqing Zhang, and William A. Ward. 2006. “A Self-Organizing Semantic Map for Word Sense Disambiguation.”* *Proceedings of the Tenth Conference on EUROLAN: Adaptive Technologies in Natural Language Processing.**

*“NLP is no longer just about understanding words—it's about understanding context, intent, and meaning.”*

That shift from an old-school, rule-based approach to one based on statistical models was transformational. But while the accuracy of NLP engines was greatly enhanced, their robustness was still limited. In the late 2000s, NLP was used mainly to classify and categorize static knowledge bases consisting of documents and websites. The world of static content was about to experience dramatic and continued change through the application of NLP technologies. NLP technology is also powering autonomous vehicles—language technology is used in systems that convert voice commands into driving commands. Natural Language Processing is being used more frequently for such voice-to-car queries, and it’s become part of safety systems that warn of approaching traffic objects
*Ratliff, Starr, Chris Meek, and Eric D. Green. 2009. “Learning from Simulated and Unsupervised Video.”* *International Journal of Computer Vision 81 (2): 229–43. doi:10.1007/s11263-008-0167-5.**

Mathias da Fontoura Costa, Neural Processing Letters* Volume 24, Number 1 (August 2006), 71–79, 2006 ├ Fine-structure of network traffic component weights.

Further Reading

For more insight into the role of AI in transforming how businesses operate, read our article on The Role of AI in Transforming Tech Businesses

How Natural Language Processing Works

Having discussed what NLP is and where it is applied, let's now explore how NLP works. NLP is essentially a multi-step pipeline that converts raw language into structured, machine-readable content. These steps often include:

Machine Learning Models

After we have extracted features, we apply </span class="uk-label uk-label-warning">machine learning or deep learning models to be able to analyze, predict, or even generate language. These models can be:

• Rule-based Systems: The old-timers of the family that work on logic and sets of grammar rules.
• Statistical Models: Use statistical methods & patterns extracted from large corpora (e.g., Naive Bayes, HMM).
• Neural Networks: Modern systems that use the power of deep learning (e.g., RNNs, LSTM, Transformers).

The Role of Transformers

Transformers changed NLP completely by enabling models to understand, learn, and capture the context and long-range dependencies in the text more effectively. Models such as BERT, GPT, and T5 dramatically boosted results in several applications, including:

• Sentiment Analysis
• Named Entity Recognition (NER)
• Text Summarization
• Question & Answering
• Language Translation

Challenges in NLP

Despite its potentials, NLP faces several challenges:

• Ambiguity: Words, phrases, or sentences that can have multiple meanings.
• Context: Grasping sarcasm, slang, or cultural nuance is still no piece of cake.
• Bias: NLP models may inherit the biases of society found in the training data.

Example Use Case: Sentiment Analysis

Let’s take a practical example on-board: analyzing customer reviews. With the help of NLP, a system could:

1. Preprocess the review (tokenization, lemmatization).
2. Extract features with the help of word embeddings.
3. Pass the vectors to a trained classifier (e.g., logistic regression or BERT).
4. Output sentiment (positive, negative, neutral).

Want to Build Something with NLP?

If you are up to applying these ideas to your own project, check out our in-depth guide to How to Set Up a WordPress Website in 2025 where you will learn how to create your first website to host NLP apps or experiments.

Real-World Applications and the Future of NLP

Natural Language Processing (NLP) is far from being some esoteric research term. Its impact on daily life is everywhere, from virtual assistants to languages being translated on-the-fly, touching billions of lives across various locations.

"NLP stands at the crossroads of human communication and machine interpretation — a crucial enabler of the AI-powered future."

Limitations

• Ambiguity: Human languages are ambiguous; the same sentence could be interpreted in numerous ways as contexts shift.
• Inherent Bias: NLP models are trained on existing data and absorb all human biases native to societies.
• Multilinguality: While NLP in English is on the cusp of incredible advances, languages like Sanskrit and Hebrew lack sufficient training data.

Future Directions for NLP

The next wave of NLP advances is in multimodal abilities — distilling insights from text, audio, and visual data to create a full-sensory AI. Large Language Models (LLMs) such as GPT-4 and beyond lead the way by demonstrating prowess in:

• Text generation that imitates human tone and intent
• Zero-shot and few-shot learning with only a handful of examples
• Real-time translation and summarization

Another major trend is in on-device NLP: AI applications that run natively on your phone for better power consumption and faster performance — think no cloud needed.

Wrap-Up

Natural Language Processing is a much more intimate technology than you might believe. Algorithms are getting smarter and more realistic, which brings NLP even closer to daily human life — automatically powering the tools we use every day.