Bag-of-words model

For the bag-of-words model in computer vision, see Bag-of-words model in computer vision.

The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.

The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier.[1] It has also been used for computer vision.[2]

An early reference to "bag of words" in a linguistic context can be found in Zellig Harris's 1954 article on Distributional Structure.[3]

  1. ^ McTear et al 2016, p. 167.
  2. ^ Sivic, Josef (April 2009). "Efficient visual search of videos cast as text retrieval" (PDF). IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 4. opposition. pp. 591–605.
  3. ^ Harris, Zellig (1954). "Distributional Structure". Word. 10 (2/3): 146–62. doi:10.1080/00437956.1954.11659520. And this stock of combinations of elements becomes a factor in the way later choices are made ... for language is not merely a bag of words but a tool with particular properties which have been fashioned in the course of its use