TF-IDF and Bag of Words: Text Representation & Search › Introduction
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Imagine you have a collection of documents, maybe product reviews, news articles, or research papers. You want to find which documents are similar, classify them by topic, or build a search system. The challenge: computers can't directly understand words.
Bag of Words solves this by treating each document as an unordered collection of word counts. It's called a "bag" because word order doesn't matter. Only how many times each word appears matters. This simple idea enables us to represent any document as a fixed-length vector of numbers.
TF-IDF builds on this foundation. It recognizes that not all words are equally informative. The word "the" appears in almost every document, so it's not useful for distinguishing between documents. But a rare word like "quantum" might be highly informative when it appears. TF-IDF automatically downweights common words and emphasizes distinctive ones.
Together, these techniques form the backbone of classical information retrieval and text classification systems. They're fast, interpretable, and surprisingly effective for many tasks.
A text representation method that converts documents into fixed-length vectors by counting word occurrences. Each dimension in the vector corresponds to a word in the vocabulary, and the value represents how many times that word appears in the document.
Term Frequency-Inverse Document Frequency. A weighting scheme that multiplies term frequency (how often a word appears in a document) by inverse document frequency (how rare the word is across the collection). This emphasizes words that are frequent in a specific document but rare overall.