Natural Language Processing (NLP)
Natural language processing (NLP) is the field of computer science and linguistics concerned with enabling computers to understand, interpret, and generate human language.
Definition¶
Natural language processing (NLP) is a subfield of artificial intelligence and computational linguistics focused on the interaction between computers and human language. NLP encompasses a broad set of tasks: understanding what language means, extracting information from it, generating new text, translating between languages, and evaluating text quality.
Core NLP Tasks Relevant to Writing¶
Text classification — categorizing text into predefined classes. AI detection is a binary text classification task (human vs. machine-generated).
Sentiment analysis — identifying whether text is positive, negative, or neutral in tone.
Named entity recognition (NER) — identifying proper nouns (people, places, organizations, dates) in text.
Text summarization — producing a condensed version of a document that preserves key information.
Machine translation — converting text from one language to another.
Readability scoring — applying formulas to estimate how difficult text is to read.
Grammar checking — identifying and correcting grammatical errors.
NLP and Large Language Models¶
Before transformer-based large language models (LLMs), NLP systems used task-specific models trained on labeled datasets. A sentiment classifier was a separate model from a translation system. LLMs represent a shift: a single large model can perform many NLP tasks, often without task-specific fine-tuning, simply by being prompted appropriately.
The writing tools built on LLMs — including AI humanizers, paraphrasers, and grammar checkers — are applied NLP systems that use the broad language understanding of LLMs to perform specific text transformation tasks.