In NLP, a corpus refers to a large and structured set of texts or documents that are used as a source of language data for analysis, training, and evaluation of natural language processing systems.

A corpus can be composed of texts from a single domain, such as scientific papers or news articles, or it can be more diverse, including texts from various sources, genres, and languages. It can be either monolingual (in a single language) or multilingual (in multiple languages).

Corpora are used in NLP to extract statistical patterns and linguistic features from the language data. This enables NLP models and algorithms to learn the characteristics of the language and make predictions or perform tasks based on that knowledge.

There are many publicly available corpora that have been compiled and annotated for specific tasks in NLP, such as sentiment analysis, named entity recognition, machine translation, and speech recognition. These corpora serve as benchmarks for the development and evaluation of new NLP systems and techniques.