NLP

AI concepts
About 1 min read

A core AI technology that enables machines to understand and generate human language. It includes NLU to capture context and intent, and NLG to generate sentences. Currently, it performs advanced translation, summarization, and reasoning based on Large Language Models (LLMs). It is an essential engine for implementing business automation by converting unstructured data into valuable information.

Also known as
Natural Language ProcessingNatural Language ProcessingNLUNLG

Detailed explanation

NLP is a technology that serves as a linguistic bridge between humans and computers. Starting from early statistical models, it has evolved through Transformer-based deep learning technology, achieving exponential performance improvements. Going beyond simple word matching, it resolves ambiguities in context and precisely captures the user's intent and emotion. In modern business environments, NLP manages the reasoning systems of intelligent agents, extracts real-time insights from vast amounts of unstructured corporate documents, and contributes to generating trustworthy information through RAG (Retrieval-Augmented Generation) technology. When choosing a technology, key evaluation metrics include language comprehension in specific domains, multilingual processing capabilities, and the ability to maintain long context windows.

Why It Matters in Tool Selection

The performance of NLP technology directly impacts user experience (UX) and work efficiency. When introducing AI to advanced tasks such as data analysis and legal/medical document review beyond simple chatbots, how accurately the tool captures the nuances of language determines the success of a project. In particular, for agglutinative languages like Korean, it is essential to check for dedicated optimization.

What to Check

  • Does it accurately understand specialized terminology from specific industries (finance, healthcare, IT, etc.)?
  • Does it accurately handle honorifics, neologisms, and contextual omissions unique to Korean?
  • Does it retain the context of the beginning of long input sentences without losing it by the end?
  • Is the response latency within an acceptable range when applied to real-time services?

Use Cases

It is used for tasks such as automatically classifying complaints by type through sentiment analysis of tens of thousands of customer service logs, or extracting key clauses from complex contracts to write summary reports.

Confusing Terms

NLU (Natural Language Understanding)

A subfield of NLP focused on analyzing the meaning, intent, and sentiment of input sentences to enable machines to 'understand' them.

NLG (Natural Language Generation)

Focuses on technology that 'generates' natural, human-readable text based on data.

Related terms

LLMTransformerRAG