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Bag-of-Words (BoW) is a simple text representation model that treats each word in a document as an independent feature, counting its frequency without considering word order or context. In BoW, each document is represented by a vector of word counts from a predefined vocabulary of unique words across the entire corpus. While BoW is easy to implement, it has limitations, such as treating common words equally with more meaningful ones, leading to a less informative representation, and resulting in high-dimensional, sparse vectors when working with large vocabularies.
On the other hand, TF-IDF (Term Frequency-Inverse Document Frequency) improves upon BoW by weighing words based on their frequency in a document (TF) and their rarity across the corpus (IDF). Words that appear frequently in a document but are rare across the entire corpus are given higher importance, helping to highlight more meaningful or unique terms. This method reduces the impact of common words (like "the" or "is"), making TF-IDF more effective in reflecting the relevance of words within the context of the entire dataset. As a result, TF-IDF is often preferred for tasks like information retrieval and document classification due to its ability to provide a more nuanced representation of text data.
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