# Bag-of-words model

The bag-of-words model is a text representation technique that converts documents into multisets of words, ignoring grammar and word order. It is a foundational method in natural language processing and machine learning, often used as a baseline for text classification and retrieval.

The bag-of-words model is a simplifying representation used in natural language processing and information retrieval. In this model, a text such as a sentence or document is represented as a multiset of its words, disregarding grammar and word order but keeping multiplicity. The name comes from the idea that a text can be viewed as a "bag" of words, where the structure is lost and only the counts of words matter. It is a common baseline in [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) and [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) for tasks such as text classification, sentiment analysis, and document retrieval.

## How it works

The construction of a bag-of-words representation begins with tokenization, the process of splitting a text into individual words or tokens. Punctuation is usually removed, and words are often lowercased. A vocabulary is then built from all unique tokens appearing in a corpus of documents. Each document is converted into a vector of counts, where each dimension corresponds to a word in the vocabulary and the value is the number of times that word appears in the document. This vector is sparse, because most documents contain only a small fraction of the vocabulary.

For example, the sentence "the cat sat on the mat" would produce a vector with counts: "the" appears twice, and "cat", "sat", "on", "mat" appear once each. The order of words is completely ignored, so "the cat sat" and "sat cat the" would yield the same representation. This loss of order information is the primary limitation of the model, but it also makes the representation simple and computationally efficient.

## Applications and limitations

Bag-of-words representations were widely used in classical [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) pipelines before the rise of [deep-learning](https://www.wikiprompt.org/wiki/deep-learning). They serve as input features for algorithms such as logistic regression, support vector machines, and naive Bayes classifiers. In information retrieval, the vector space model, introduced by Gerard Salton and colleagues in 1975, represents documents and queries as bag-of-words vectors and compares them by cosine similarity.

The model has several known limitations. It cannot capture word order, so phrases like "not good" and "good not" are treated identically. It also ignores semantics: synonyms such as "car" and "automobile" are treated as separate dimensions, while polysemous words such as "bank" are conflated. The resulting vectors are high-dimensional and sparse, which can lead to overfitting and poor generalization when the vocabulary is large. To mitigate these issues, practitioners often apply weighting schemes such as term frequency-inverse document frequency (TF-IDF) or use n-grams to capture short sequences.

## Variants and extensions

Several extensions address the weaknesses of the basic bag-of-words model. TF-IDF replaces raw counts with weights that downweight common words and emphasize rare ones. N-gram models extend the bag to include contiguous sequences of n words, preserving some local order information. The hashing trick maps words to a fixed-size vector using a hash function, avoiding the need to store a vocabulary. These methods remain useful in many applications, particularly when labeled data is scarce.

Another important extension is the use of word embeddings, which map words to dense low-dimensional vectors that capture semantic similarity. Unlike bag-of-words, embeddings preserve some relationships between words. However, embeddings are typically learned using [neural-network](https://www.wikiprompt.org/wiki/neural-network) methods, which require more data and computation. The bag-of-words model remains a strong baseline: in many text classification tasks, a linear classifier on bag-of-words features can match the performance of more complex models.

## Relationship to modern AI

With the advent of [large-language-model](https://www.wikiprompt.org/wiki/large-language-model)s and the [transformer](https://www.wikiprompt.org/wiki/transformer) architecture, bag-of-words representations have largely been superseded for complex natural language understanding tasks. The transformer architecture, introduced in 2017, uses [positional-encoding](https://www.wikiprompt.org/wiki/positional-encoding) to inject word order information and [multi-head-attention](https://www.wikiprompt.org/wiki/multi-head-attention) to model relationships between all words in a sequence. These mechanisms allow models to capture context and long-range dependencies that are invisible to a bag-of-words representation. Architectures such as [sequence-to-sequence](https://www.wikiprompt.org/wiki/sequence-to-sequence) and [encoder-decoder](https://www.wikiprompt.org/wiki/encoder-decoder) models also rely on learned embeddings rather than count-based vectors.

Nevertheless, the bag-of-words model continues to influence modern AI. It is often used as a feature extraction step in hybrid systems, and its simplicity makes it a useful pedagogical tool for understanding machine-learning concepts. The model also appears in [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) applications as a baseline for evaluating more sophisticated representations. Even as [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) advances, the bag-of-words model remains a fundamental concept in the history of natural language processing.

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Source: https://www.wikiprompt.org/wiki/bag-of-words-model
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-14T04:21:12.066023+00:00