Chunking

Chunking is an essential preprocessing step in NLP that involves breaking down text into smaller, manageable units, or “chunks.” This process is crucial for various applications, including text summarization, sentiment analysis, information extraction, and more.

Why is chunking becoming more and more important? By breaking down large documents, chunking enhances manageability and efficiency, particularly for models with token limits, preventing overload and enabling smoother processing. It also improves accuracy by allowing models to focus on smaller, coherent segments of text, which reduces noise and complexity compared to analyzing entire documents. Additionally, chunking helps maintain context within each segment, which is essential for tasks such as machine translation and text generation, ensuring that the model comprehends and processes the text effectively.

Chunking can be implemented in many different ways; for instance, summarization may benefit...