Bag of Word

The "bag-of-words" (BoW) model is a fundamental approach in natural language processing that represents text as an unordered collection of its constituent words, focusing on word frequency rather than grammatical structure. Current research explores BoW's application across diverse tasks, including text classification, information retrieval, and even image analysis, often comparing its performance against more sophisticated models like large language models (LLMs) and neural networks. While LLMs often outperform BoW in many scenarios, research highlights BoW's continued relevance due to its simplicity, interpretability, efficiency, and surprisingly strong performance in specific contexts, particularly with low-resource languages or computationally constrained environments. This makes BoW a valuable baseline and a continuing area of investigation for improving efficiency and interpretability in various NLP applications.