Natural Language Processing
The bag-of-words (BoW) model is a simplifying representation used in natural language processing where a text is treated as an unordered collection of words, disregarding grammar and word order but keeping multiplicity. This model helps in converting text into numerical feature vectors that can be processed by machine learning algorithms. By counting the frequency of words in a document, BoW enables the extraction of semantic meaning in a format suitable for various NLP tasks, including sentence and document embeddings.
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