Do you need to create a dictionary (vocabulary) to produce a bag-of-words representation of text corpus?

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BlackTom AI · Accepted Answer

The question asks whether a dictionary (vocabulary) is needed to produce a bag-of-words representation for a text corpus.
Option 1: True. This is the correct notion because bag-of-words typically requires constructing a vocabulary of unique terms in the corpus, with each term assigned an index. Documents are then represented as vectors counting occurrences of those terms, so without a dictionary there is no fixed dimension or mapping to count.
Option 2: False. This would imply that you can form a bag-of-words without knowing which terms exist in the corpus or without a consistent mapping. In practice, without a vocabulary, you wouldn’t have a stable feature space, and counts would be ambiguous or untrackable across documents, defeating the purpose of a fixed-length representation.
Option 3: If interpreted as requiring a dictionary only after processing, this would miss the core step where the vocabulary is established to define the vector space. The dictionary is the foundational component of the representation, not something added afterwards.
Option 4: Some alternative representations (like raw character n-grams or hashed representations) can bypass an explicit dictionary, but these do not constitute the canonical bag-of-words with a traditional vocabulary-based count vector. If the question strictly means the standard bag-of-words with a vocabulary, the dictionary remains essential.

SP2025.B69.DAT.562.24 COMPLETE AFTER VIDEO 4: Bag-of-Words (Module 2 Quiz #3)

Do you need to create a dictionary (vocabulary) to produce a bag-of-words representation of text corpus?

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