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.
[2] An early reference to "bag of words" in a linguistic context can be found in Zellig Harris's 1954 article on Distributional Structure.
Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple tasks that do not require word order.
Additionally, for the specific purpose of classification, supervised alternatives have been developed to account for the class label of a document.
[4] Lastly, binary (presence/absence or 1/0) weighting is used in place of frequencies for some problems (e.g., this option is implemented in the WEKA machine learning software system).