Bag-of-words model

The bag-of-words model is a model of text which uses a representation of text that is based on an unordered collection (or "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus any non-trivial notion of grammar[clarification needed]) but captures 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, for example, 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.

Example implementation

The following models a text document using bag-of-words. Here are two simple text documents:

(1) John likes to watch movies. Mary likes movies too.
(2) Mary also likes to watch football games.

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



Representing each bag-of-words as a JSON object, and attributing to the respective JavaScript variable:


Each key is the word, and each value is the number of occurrences of that word in the given text document.

The order of elements is free, so, for example {"too":1,"Mary":1,"movies":2,"John":1,"watch":1,"likes":2,"to":1} is also equivalent to BoW1. It is also what we expect from a strict JSON object representation.

Note: if another document is like a union of these two,

(3) John likes to watch movies. Mary likes movies too. Mary also likes to watch football games.

its JavaScript representation will be:


So, as we see in the bag algebra, the "union" of two documents in the bags-of-words representation is, formally, the disjoint union, summing the multiplicities of each element.



Implementations of the bag-of-words model might involve using frequencies of words in a document to represent its contents. The frequencies can be "normalized" by the inverse of document frequency, or tf–idf. Additionally, for the specific purpose of classification, supervised alternatives have been developed to account for the class label of a document. 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).

Python implementation

# Make sure to install the necessary packages first
# pip install --upgrade pip
# pip install tensorflow
from tensorflow import keras
from typing import List
from keras.preprocessing.text import Tokenizer

sentence = ["John likes to watch movies. Mary likes movies too."]

def print_bow(sentence: List[str]) -> None:
    tokenizer = Tokenizer()
    sequences = tokenizer.texts_to_sequences(sentence)
    word_index = tokenizer.word_index 
    bow = {}
    for key in word_index:
        bow[key] = sequences[0].count(word_index[key])

    print(f"Bag of word sentence 1:\n{bow}")
    print(f"We found {len(word_index)} unique tokens.")


Hashing trick

A common alternative to using dictionaries is the hashing trick, where words are mapped directly to indices with a hashing function. Thus, no memory is required to store a dictionary. Hash collisions are typically dealt via freed-up memory to increase the number of hash buckets[clarification needed]. In practice, hashing simplifies the implementation of bag-of-words models and improves scalability.

See also

This page was last updated at 2024-02-24 08:35 UTC. Update now. View original page.

All our content comes from Wikipedia and under the Creative Commons Attribution-ShareAlike License.


If mathematical, chemical, physical and other formulas are not displayed correctly on this page, please useFirefox or Safari