How to Chunk Text Data — A Comparative Analysis

The ‘Text chunking’ process in Natural Language Processing (NLP) involves the conversion of unstructured text data into meaningful units. This seemingly simple task belies the complexity of the various methods employed to achieve it, each with its strengths and weaknesses. At a high level, these methods typically fall into one of two categories. The first, rule-based methods, hinge on the use of explicit separators such as punctuation or space characters, or the application of sophisticated systems like regular expressions, to partition text into chunks. The second category, semantic clustering methods, leverages the inherent meaning embedded in the text to guide the chunking process. These might utilize machine learning algorithms to discern context and infer natural divisions within the text. In this article, we’ll explore and compare these two distinct approaches to text chunking. We’ll represent rule-based methods with NLTK, Spacy, and Langchain, and contrast this with two different semantic clustering techniques: KMeans and a custom technique for Adjacent Sentence Clustering. The goal is to equip practitioners with a clear understanding of each method’s pros, cons, and ideal use cases to enable better decision-making in their NLP projects. <a href="https://towardsdatascience.com/how-to-chunk-text-data-a-comparative-analysis-3858c4a0997a">Visit Now</a>