Bag of words

The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision.

The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier.

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

The following models a text document using bag-of-words.

Here are two simple text documents:

Based on these two text documents, a list is constructed as follows:

In practice, the Bag-of-words model is mainly used as a tool of feature generation. After transforming the text into a "bag of words", we can calculate various measures to characterize the text. The most common type of characteristics, or features calculated from the Bag-of-words model is term frequency, namely, the number of times a term appears in the text. For the example above, we can construct the following two lists to record the term frequencies of all the distinct words:

Each entry of the lists refers to count of the corresponding entry in the list (this is also the histogram representation). For example, in the first list (which represents document 1), the first two entries are "1,2". The first entry corresponds to the word "John" which is the first word in the list, and its value is "1" because "John" appears in the first document 1 time. Similarly, the second entry corresponds to the word "likes" which is the second word in the list, and its value is "2" because "likes" appears in the first document 2 times. This list (or vector) representation does not preserve the order of the words in the original sentences, which is just the main feature of the Bag-of-words model. This kind of representation has several successful applications, for example email filtering.

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