The Bag-of-Words (BoW) model is a fundamental technique used in Natural Language Processing (NLP) for text representation. It simplifies text data by converting it into a set of words, disregarding grammar and word order, while preserving multiplicity. Each unique word in the document is treated as a feature, and the model counts how many times each word appears. Common use cases include document classification, sentiment analysis, and information retrieval, where it helps in transforming textual data into a structured format suitable for machine learning algorithms.
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