What is NLP in simple terms?

NLP is the field of building computer systems that classify, search, transform or generate written and spoken human language.

What is a token in NLP?

A token is a unit processed by a model, such as a word, subword, character or punctuation mark. Tokenization rules depend on the model.

What is an embedding?

An embedding is a dense numerical vector used to represent a token, sentence or document so a model can learn or compare useful relationships.

What are the main NLP tasks?

Common tasks include classification, entity recognition, semantic search, question answering, summarization, translation and text generation.

Is NLP the same as a large language model?

No. Large language models are one family of models used in NLP. NLP also includes rules, statistical methods, search, smaller neural models and structured pipelines.

What is the difference between training and inference?

Training updates model parameters from data. Inference applies a trained model to new input without changing its parameters.

How should a beginner evaluate an NLP model?

Start with task-specific metrics and inspect errors by class, language and operating condition. Add human review and downstream outcome measures where decisions matter.

Korean · Rusaka

Natural Language Processing의 기본 이해

Natural Language Processing (NLP)는 언어학, 컴퓨터 과학 및 인공 지능을 융합하는 매혹적인 분야입니다. 텍스트를 분석하고 응답을 생성 할 수있는 응용 프로그램을 개발하는 것이 중요하다, 인간 언어를 이해하고 해석 할 수 있습니다.

Natural Language Processing의 기본 이해