AI Sketchbook Series #2- Text Representation - Bag-of-Words TF-IDF

For this next installment in our series demystifying AI concepts, we delve into how the meaning and importance of words within a document can be numerically captured. Bag-of-Words (BoW) offers a straightforward approach by representing a text as the multiset of its words, essentially counting their occurrences. This visual explanation will illustrate how a document can be transformed into a numerical vector based purely on word frequency, providing a basic yet powerful way to compare and analyze texts. Building upon this, Term Frequency-Inverse Document Frequency (TF-IDF) refines this representation by not only considering how often a word appears in a specific document (Term Frequency) but also how unique it is across a collection of documents (Inverse Document Frequency). This adjustment gives more weight to words that are distinctive to a particular text. By visualizing these techniques, we’ll gain a clearer understanding of how machines can begin to discern the content and significance of words in a corpus, which is fundamental in tasks like document classification and information retrieval.