The bag-of-words model is a simplifying assumption used in natural language processing and information retrieval. In this model, a text (such as a sentence or a document) is represented as an unordered collection of words, disregarding grammar and even word order. The bag-of-words model is used in some methods of document classification. When a Naive Bayes classifier is applied to text, for example, the conditional independence assumption leads to the bag-of-words model.

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  • The bag-of-words model is a simplifying assumption used in natural language processing and information retrieval. In this model, a text (such as a sentence or a document) is represented as an unordered collection of words, disregarding grammar and even word order. The bag-of-words model is used in some methods of document classification. When a Naive Bayes classifier is applied to text, for example, the conditional independence assumption leads to the bag-of-words model. Other methods of document classification that use this model are latent Dirichlet allocation and latent semantic analysis. An early reference to "bag of words" in a linguistic context can be found in Zellig Harris's 1954 article on Distributional Structure.
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  • The bag-of-words model is a simplifying assumption used in natural language processing and information retrieval. In this model, a text (such as a sentence or a document) is represented as an unordered collection of words, disregarding grammar and even word order. The bag-of-words model is used in some methods of document classification. When a Naive Bayes classifier is applied to text, for example, the conditional independence assumption leads to the bag-of-words model.
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  • Bag of words model
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