The Bag-of-Words (BoW) model is a basic yet widely used technique in Natural Language Processing (NLP) for text representation. It converts text into a numerical format by creating a vocabulary of unique words in the document and representing each text as a vector of word frequencies.
In this representation, each position in the vector corresponds to a word in the vocabulary, and the value at that position indicates the word's count in the document. This model disregards grammar and word order, considering only the presence or absence of words. Consequently, it captures word occurrences but fails to encode context or semantics. BoW is typically used in text classification tasks and for computing document similarity. Despite its simplicity, it can become inefficient with large vocabularies, leading to sparse and high-dimensional vectors.