What is the main purpose of a tokenizer during preprocessing in NLP?

The main purpose of a tokenizer during the preprocessing phase in natural language processing (NLP) is to split text into tokens for analysis. Tokenization is the process of breaking down a stream of text into smaller pieces, known as tokens. These tokens can be words, phrases, or even characters, depending on how the tokenizer is configured.

This initial step is crucial as it allows for better manipulation and analysis of the text. By breaking the text into manageable units, various subsequent NLP tasks, such as sentiment analysis, classification, or named entity recognition, can be performed more efficiently.

For example, if the input text is a sentence, the tokenizer will separate it into individual words or terms, which can then be counted, compared, or transformed in various ways for further analysis. This process supports the overall goal of understanding and extracting insights from natural language data.

The other options have specific purposes but do not directly describe the core function of a tokenizer. Splitting text into sentences, converting text into a feature vector, and removing stop words are distinct preprocessing tasks that may follow or complement tokenization, but they do not define its main role.