Tokenization (NLP)

Tokenization involves dividing written language into its constituent parts—known as tokens—enabling Natural Language Processing (NLP) algorithms to more effectively parse and understand text data. The method used for tokenizing can vary significantly across languages and applications: some systems may separate based on whitespace and punctuation, while others might use machine learning techniques to determine segmentation. Efficient tokenization is critical since it directly impacts how well an NLP model will function in executing tasks such as translation, sentiment analysis, or conversation generation.