Yes, sentence segmentation is typically performed before tokenization in natural language processing (NLP) tasks. Sentence segmentation involves dividing a paragraph or a document into individual sentences, while tokenization involves breaking down each sentence into smaller units called tokens, such as words or punctuation marks.

  The reason for performing sentence segmentation before tokenization is to ensure that each sentence is analyzed and processed independently. By breaking the text into sentences first, it becomes easier to apply further linguistic analysis, such as part-of-speech tagging, named entity recognition, or sentiment analysis, at the sentence level.

  Moreover, tokenization relies on sentence boundaries to correctly identify and separate tokens in a sentence. For example, in the sentence "I have a cat and a dog," tokenization would break it down into tokens like "I," "have," "a," "cat," "and," "a," and "dog." If the sentence segmentation is not performed beforehand, the entire paragraph or document would be considered as a single sentence, making tokenization more challenging.

  In some cases, sentence segmentation and tokenization can be performed simultaneously using algorithms that take into account punctuation marks, capitalization, and other language-specific features. However, the general practice is to first segment the text into sentences and then tokenize each sentence, as it allows for more accurate language analysis and processing.