Beginner · NLP
Bag-of-words model
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A text representation that counts word occurrences, ignoring grammar and word order, to capture content.
Technical Definition
A text representation that counts word occurrences, ignoring grammar and word order, to capture content.
How it works
The bag-of-words (BoW) model is a common technique in natural language processing and information retrieval. It represents text as a collection of its words, where the frequency of each word is counted, but the order in which words appear is disregarded. This simplifies text into a feature vector suitable for machine learning tasks like classification.
Related Concepts
- Feature Extraction — Deriving informative numerical signals from raw data for use as model inputs.
- Natural language processing (NLP) — A field of AI enabling computers to understand, interpret, and generate human language.
- Text Classification — The task of categorizing text documents into predefined classes or labels.