Natural Language Processing (NLP) is a technique within the field of Artificial Intelligence (AI) that has expanded over the past 20 years as our use of computers and digital devices has grown. In simple terms, NLP teaches computers how to understand human language. NLP powers language translation apps, personal digital assistants like Siri or Google Assistant and voice-controlled devices at home (Locke et al., 2021). Whenever you speak to your phone or it suggests words while you are typing, that is NLP at work.
NLP works by helping computers break down and understand human language step by step. When we type or speak, our words come in an unstructured form and NLP converts this unstructured text into a format a computer can understand. To do this, NLP uses machine learning algorithms that analyse large amounts of text and learn patterns from it so they can recognise words, understand context, and make reasonable guesses about what we mean.
The main steps in this process involve text processing, where NLP cleans and prepares the text through tokenization or word reduction, followed by syntactic analysis, which interprets deeper meaning through context, tone, and intent to extract useful information for task such as translation and question answering.
Through these steps, NLP transforms raw text into useful information and allowing machines to understand language more naturally.
Importance of NLP
NLP is important because it allows people to communicate with computers in a natural and simple way. Instead of using codes or commands, we can just type or speak like we normally do, and the system understands us. NLP also helps search engines figure out what we really mean when we type something, so the results we get are more accurate and useful. It also makes it easier to read and understand large amounts of text such as customer feedback or social media posts, so organisations can see trends, common issues and public opinions.
NLP also helps automate many everyday tasks. It can summarise long documents, sort emails, detect emotions in text and translate languages instantly. Many tools we use today, like chatbots and virtual assistants, rely on NLP to understand questions, respond correctly and offer helpful suggestions. All of this makes technology more helpful, faster and easier for people to use, no matter what language they speak (Mohana, 2024).
Challenges with NLP
However, NLP also faces challenges because human language is naturally messy and unpredictable. People often make spelling mistakes, use informal shortcuts, or write in different styles, which can confuse systems that expect clear and consistent text. Different languages also express ideas differently and NLP models may pick up hidden biases from the data they are trained on. Words with multiple meanings add further difficulty because computers cannot interpret nuance as easily as humans.
There are technical challenges that make NLP difficult to manage (shaip, 2025). An NLP system may confidently give an incorrect answer, known as a false positive. Good training data is essential, and if the data is limited, inaccurate or biased, the model will learn the wrong patterns. For NLP to perform well, it needs large, clean and diverse datasets; otherwise, the results may be unreliable.
Real-World Applications of NLP
Top 10 Application of AI in NLP (source: https://www.solulab.com/top-applications-of-natural-language-processing/)
Behind many modern technologies, NLP helps apps and devices understand language and respond more intelligently. It helps computers understand human language so they can analyse opinions, classify text, extract important information, recognize speech, and translate documents. Businesses use NLP for sentiment analysis to monitor customer feelings, for topic classification to group survey responses, and for text extraction to pull out names, dates, or numbers from documents. These tools save time, reduce manual work, and give companies clearer insights into what customers think and need.
NLP also supports many daily operations such as market intelligence, hiring, and customer support. Recruiters use NLP to scan resumes faster and more fairly, while marketers analyse online conversations to spot trends and customer pain points. Speech recognition helps convert spoken words into text for better communication, and spam filters rely on NLP to keep inboxes clean. Customer support systems use NLP to route calls and answer basic questions automatically. NLP makes technology smarter, faster, and more helpful in handling everyday language tasks (Shipra, 2025).
By Dr. Wan Noor Hamiza Wan Ali, Senior Lecturer, Faculty of Artificial Intelligence (FAI) Universiti Teknologi Malaysia (UTM)
