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Bag of Words
Bag of Words (BoW) is a natural language processing (NLP) strategy for converting a text document into numbers that can be used by a computer program. BoW is often implemented as a Python dictionary. Each key in the dictionary is set to a word, and each value is set to the number of times the word appears. The BoW model is one of the most useful ways to convert text data for use by machine learning algorithms. In this context, text words are referred to as tokens and the entire process of representing a sentence as a bag of words vector (a string of numbers) is known as tokenization. Techopedia Explains Bag of Words (BoW) BoW models are concerned with whether a known word occurs in a document and how many times it occurs -- not the order in which it appears, nor its context. BoW plays an important role in natural language processing, information retrieval from documents and document classification.
Contributeurs: Patrick Drouin, wiki
