Language is the most natural means of communication that humans use every day. However, for computers, understanding human language is not easy. That's why technology Natural Language Processing (NLP) present-a branch of artificial intelligence (AI) that allows computers to understand, interpret, and respond to human language in a natural way.
Without realizing it, we interact with NLP almost every day. When using Google Translate, talking to Siri, typing messages with autocorrect, to reading more accurate search results — it all works thanks to NLP.
What is NLP?
NLP is an abbreviation of Natural Language Processing, i.e. the inner plane Artificial Intelligence AI focuses on the ability of computers to understand and produce human language.
With NLP, machines not only read text, but also understand the meaning, context, and emotions behind words.
For example, when someone types “today's weather in Malang”, the NLP system will recognize that the user is looking for weather information, not just reading word for word.
In short, NLP teaches computers to understand humans-not just to read data.
Natural Language Processing According to Experts
Some experts define NLP from different points of view, but with the same core: the ability of machines to understand natural language.
- Russell & Norvig (2016): “NLP is a branch of AI that focuses on the interaction between computer and human language.”
- Jurafsky & Martin (2023): "NLP includes understanding the meaning, context, and structure of language using linguistic approaches as well as machine learning.”
From both views, it can be concluded that NLP is a bridge that connects linguistics, computer science, and AI so that machines can “communicate " with humans more naturally.
Functions and goals of NLP
The main goal of NLP is to help machines understand language like humans. Its main functions include:
- Understanding text and context: recognize the meaning of sentences and the relationship between words.
- Natural Language Generation (NLG): for example an AI system that writes automatic text.
- Analyzing emotions or sentiments: used on social media to find out public opinion.
- Improving human and machine interaction: like a customer service chatbot.
With NLP, computer systems can read thousands of documents, analyze content, and present conclusions quickly — something humans can't do in a short amount of time.
What are the 4 Pillars of NLP?
In practice, NLP is built on four main pillars interconnected:
- Speech Recognition
Converts human speech into text.
Example: features voice typing in Google Docs or voice assistants like Alexa. - Natural Language Understanding (NLU)
Understand the meaning of sentences and the context of speech.
Example: a chatbot that can distinguish between “I want to buy a product “and”I want to complain about a product". - Natural Language Generation (NLG)
Produce new text or sentences that sound natural.
Examples: systems that create automated news summaries or answer customer questions. - Machine Translation
Translate languages automatically from one language to another.
Contoh: Google Translate dan DeepL.
These four pillars form the main foundation for a variety of modern AI systems that interact with humans.
NLP methods
NLP approaches are constantly evolving from traditional to method-based deep learning. Here are some of them:
- Rule-Based NLP
Rely on linguistic and grammatical rules written manuals.
It is suitable for simple systems such as grammar checking (grammar check). - Statistical NLP
Using probability methods to understand language patterns based on big data.
Example: text prediction system on short messages. - Neural Network & Deep Learning NLP
Is a modern approach used in models such as GPT, BERT, dan T5.
This method allows the computer to understand the context of sentences very accurately, even in everyday language.
Examples of Natural Language Processing (NLP)
Here are some real examples of the use of NLP that we often encounter:
- Chatbot Customer Service
Chatbots like ChatGPT, Zendesk, or Botika utilize NLP to understand customer questions and provide automated answers. - Automatic Language Translator
Apps like Google Translate use NLP to translate sentences with attention to context, not just word for word. - Social Media Sentiment Analysis
Companies use NLP to monitor public opinion of their products on Twitter or Instagram. - Speech-to-Text dan Voice Assistant
Siri, Alexa, and Google Assistant take advantage of NLP to recognize voices and understand user intent. - Autocorrect & Smart Compose
When you type a message and appears sentence suggestion or autocorrect — it is a simple form of NLP.
How is NLP applied in everyday life?
NLP has now become part of our lives, even without us realizing it. Some examples of its application:
- On smartphones: features predictive text and voice typing.
- By email: system smart reply Gmail that suggests automatic replies.
- In business: 24-hour chatbot system to serve customers.
- In the world of work: automatically analyze text-based documents, reports or data.
Even in the world audit and finance, NLP is used for analyze financial statements, detect anomalies, and extract important data from long documents automatically - matters relevant to the Audithink service.
Challenges and new developments in NLP
Although already sophisticated, NLP still faces a number of challenges:
- Language ambiguity: one word can have many meanings (example: “can " Means able or snake venom).
- Cultural context differences: the meaning of the word may change depending on the region.
- Data privacy: the use of text data needs protection so that it is not misused.
However, the development Large Language Models (LLM) such as GPT-4, Gemini, and Claude opening a new era for NLP that is increasingly accurate and contextual.
NLP has also been developed to understand multilingual, even local dialects, making it increasingly relevant for a wide range of sectors — including audit analysis, documentation and Business Research.
Conclusion
Natural Language Processing (NLP) is a technology that bridges humans and machines through language. By understanding natural language, computers can do many things that previously only humans could do — from answering questions to writing reports.
In the future, NLP will become an essential part of any AI-based system, especially in document processing, business communication, and text-based data analysis.
Want to know how NLP can improve the efficiency of your data auditing and analysis?
Technology Natural Language Processing now it is not only used by large technology companies. In the world of auditing and business, NLP is able to help analyze thousands of reports and documents quickly, accurately and without bias.
Audithink presents solutions based on Artificial Intelligence and NLP to help organizations understand audit data, detect irregularities, and simplify the reporting process.
Visit Audithink home page to learn more, or contact our team to have a discussion about how NLP can help digital transformation in your business.
