Which of the following is a common text preprocessing technique?

Tokenization is a fundamental text preprocessing technique commonly used in Natural Language Processing (NLP). It involves breaking down a text into smaller units, typically words or phrases, which are referred to as tokens. The purpose of tokenization is to convert a stream of text into a format that can be more easily analyzed and understood by NLP algorithms.

By tokenizing text, you can facilitate various tasks such as sentiment analysis, text classification, and machine translation. Each token can be individually processed, allowing algorithms to identify patterns, similarities, and structures in the data. Tokenization serves as a foundational step in preparing text for further analysis, as it simplifies the complexity of language into manageable pieces.

The other options, while relevant in different contexts, do not serve as common preprocessing techniques specifically within the realm of NLP. Encryption focuses on securing data, data anonymization is about protecting personal information, and compression is aimed at reducing file sizes. These techniques do not inherently transform textual data into a format ready for analysis like tokenization does.