What is meant by text classification in NLP?

Text classification in natural language processing refers to the process of assigning predefined categories or labels to a piece of text based on its content. This technique is widely used in various applications, such as sentiment analysis, spam detection, topic labeling, and more, where it's essential to determine the category to which a specific document or text belongs.

In this context, the method involves training models on labeled datasets, allowing the models to learn the characteristics that distinguish different categories. Once trained, these models can automatically categorize new texts based on the learned patterns. For example, in sentiment analysis, texts can be classified as positive, negative, or neutral based on their content.

The other options do not accurately describe text classification. Sorting texts by length focuses solely on the physical characteristics of the text rather than its content. Translating text pertains to converting it from one language to another, which is a different NLP function known as machine translation. Evaluating text for grammatical errors relates to proofreading and does not involve categorization of content. Therefore, the definition of text classification is best captured by the correct choice.