What is tokenization in NLP?

Tokenization in Natural Language Processing (NLP) refers to the process of breaking down text into smaller units called tokens. These tokens can be words, phrases, or even characters. This foundational step is crucial as it transforms text data into a format that can be more easily analyzed and utilized in various NLP tasks.

The choice that correctly identifies tokenization as converting words or phrases into numeric identifiers for modeling aligns with the broader context of preparing text data for further machine learning processes. When raw text is tokenized, each unique token often gets assigned a numeric identifier, which allows algorithms to process and analyze the text effectively. This is an essential part of creating representations of natural language that can be used for tasks like classification, sentiment analysis, and more.

While other choices might describe important NLP concepts, they do not accurately represent tokenization itself:

• Simplifying complex sentences pertains to parsing or abstraction techniques, rather than tokenization.

• Translating words relates to language translation tasks, not the act of breaking text into tokens.

• Analyzing sentence structure involves syntactic parsing or grammar analysis, which is distinct from the process of tokenization.

Understanding this distinction helps highlight the significance of tokenization as a preliminary step in preparing text data for various NLP applications.