What does pre-processing in NLP typically involve?

Pre-processing in Natural Language Processing (NLP) is a crucial step that involves preparing raw text for modeling by enhancing its quality and usability. The practice typically includes techniques such as removing stop words—common words that do not add significant meaning to the data, like "and," "the," or "is"—and tokenization, which is the process of breaking down text into individual terms or tokens.

By performing these operations, text data is transformed into a structured format that can be effectively analyzed and modeled. This enhances the algorithm's ability to learn patterns from the data and overall improves the performance of NLP tasks.

Other options, while they involve relevant aspects of NLP and machine learning, do not pertain directly to the initial stage of preparing textual data for analysis. For instance, encoding data for machine learning is more about transforming processed data into numerical formats that algorithms can work with, while testing models pertains to the evaluation phase after models have been trained. Collecting user feedback for optimization is an essential iterative process post-deployment but does not relate to pre-processing, which strictly focuses on preparing the data before any model training occurs.