What is a primary function of tokenization in NLP?

Tokenization is a crucial step in the natural language processing (NLP) pipeline, primarily serving the function of breaking down a continuous stream of text into smaller, manageable units known as tokens. These tokens can be words, phrases, symbols, or even characters, depending on the granularity required by the analysis. By segmenting text into tokens, subsequent processing tasks, such as analyzing word frequency, understanding context, and building models, become significantly more feasible and organized.

This function aids in preprocessing text data, which is essential for various NLP applications, including machine learning tasks. For instance, if a system needs to analyze or classify text, having individual tokens allows for clearer input data that can better reveal patterns or structures in the language used.

In the context of understanding the various options related to tokenization, the other choices pertain to different NLP tasks. For instance, categorizing tokens into parts of speech involves a distinct level of linguistic analysis, while sentiment interpretation and syntactic parsing represent further analyses that build on foundational tokenization.