What is the purpose of a corpus in NLP?

The purpose of a corpus in Natural Language Processing (NLP) is to serve as a large collection of texts that are utilized for training and evaluating NLP models. This body of text provides the necessary linguistic data that allows models to learn language patterns, semantics, and grammar. A well-constructed corpus is indispensable for tasks such as language modeling, sentiment analysis, and machine translation, as it provides the foundational data from which algorithms can derive insight and make predictions.

In NLP, the quality and diversity of the corpus directly influence the performance of the model. By training on a broad and representative text collection, models can generalize better and perform effectively on unseen data. Evaluating a model’s performance also relies heavily on a corpus, as it provides the set of examples needed to benchmark and assess how well the model understands and processes language.

Other options, while relevant in different contexts, do not align with the function of a corpus in NLP. Evaluating website performance, creating audio samples for speech recognition, and improving image processing techniques pertain to different fields and are not connected to the primary role that a corpus plays in language processing and machine learning tasks involving text.