Ground Truth (NLP)

In Natural Language Processing (NLP), "Ground Truth" refers to the accurate, manually verified data used as a benchmark for training and evaluating algorithms. It's the trusted standard that tells the model what the correct output should be for given inputs, such as correct labels for text or speech. This data is essential because it helps NLP systems learn patterns and ensures their predictions are accurate. Think of it as the definitive answer key against which the system's performance is measured. Accurate ground truth data is critical for developing reliable and effective NLP applications.