{"id":83513,"date":"2025-08-27T12:01:34","date_gmt":"2025-08-27T06:31:34","guid":{"rendered":"https:\/\/www.the-next-tech.com\/?p=83513"},"modified":"2025-09-03T16:34:50","modified_gmt":"2025-09-03T11:04:50","slug":"does-chunking-in-nlp-exist","status":"publish","type":"post","link":"https:\/\/www.the-next-tech.com\/machine-learning\/does-chunking-in-nlp-exist\/","title":{"rendered":"Does Chunking In NLP Exist In 2025? Or Is It Overtaken By Modern LLMs?"},"content":{"rendered":"

Chunking in NLP still surviving while modern LLMs evolving at a greater speed.<\/strong><\/p>\n

During the phase of symbolic NLP<\/strong>, natural language processing was entirely rule-based and symbolic. It worked on written grammar rules, dictionaries, and syntax trees. For example., ELIZA<\/a>, a chatbot that answer based on pattern matching rules.<\/p>\n

But the challenge it was not accurate and scalable. Then emerges the phase of statistical NLP<\/strong>, natural language processing based on N-gram language model<\/a> that predicts the next possible words in the sentence using probability derived Machine Learning and Statistical techniques.<\/p>\n

Though it has one big limitation that it cannot predict next word that are not presented in the datasets. This challenge emerged the phase of deep learning era.<\/p>\n

The deep learning NLP<\/strong> was based on RNN (Recurrent Neural Networks) that uses sequential learning method. In its architecture, next word prediction happen sequentially. Google Translate<\/strong>, for instance, initially used phrase-based statistical machine translation before moving to deep learning revolution.<\/p>\n

With ongoing research to improve deep learning NLP led to the development of Transformers<\/strong> and models like BERT (2018) and GPT (2018), which shifted NLP from handcrafted rules to end-to-end deep learning.<\/p>\n

In this blog, we will learn about traditional NLP working in the context of text processing.<\/p>\n

Before we begin, following is a natural flow of traditional Natural Language Processing:<\/p>\n

Tokenization \u2192 PoS Tagging \u2192 Chunking \u2192 Parsing<\/em><\/p>\n

Let\u2019s understand each of them in detail\u2026!<\/p>\n

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Table of Contents<\/p>\n<\/i><\/a><\/span><\/div>\n