What is a bag-of-words representation of a document in a text corpus? Choose one best answer:

Question

BlackTom AI · Accepted Answer

Starting with a general framing, the bag-of-words model represents documents as word-frequency vectors derived from a predefined dictionary. 
Option 1: 'List of unique tokens observed in a document' describes a set of tokens, not their counts, so it captures presence but not frequency—this omits the essential 'counts' aspect of BoW.
Option 2: 'List of unique tokens observed in a text corpus' shifts the focus to the entire corpus rather than an individual document; BoW is typically constructed per document using a shared dictionary, so this is not the standard representation.
Option 3: 'Counts of dictionary tokens for a specific document' correctly emphasizes two key pieces: a fixed dictionary (the vocabulary) and counts (frequencies) of those dictionary tokens within that particular document, which is the core of the BoW representation.
Option 4: 'Counts of unique tokens in a document' would imply counting only distinct tokens or possibly focusing on a binary presence metric, which does not capture the full idea of token frequencies across the dictionary; BoW uses counts for each dictionary token, not just a total count or a count of unique tokens.

What is a bag-of-words representation of a document in a text corpus? Choose one best answer:Single choice

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