Bag-of-Words (BoW) Vector-Feature Creation Function
(Redirected from bag-of-words model)
Jump to navigation
Jump to search
A Bag-of-Words (BoW) Vector-Feature Creation Function is a distributional vectorization function that maps a text item to a bag-of-words vector (based on a bag-of-words count function).
- AKA: BoW Mapping Model.
- Context:
- It can range from (typically) being a Corpus-based Bag-of-Words Mapping to being a Manually Created Bag-of-Words Mapping.
- It can range from being a Document-level BoW Function to being a Sentence-level BoW Function to being a Text Window-based BoW Function.
- It can be created by a Bag-of-Words Mapping System.
- It can be a member of a Word Vector Feature Space.
- It can (in the abstract) define a Bag-of-Words Vector Space.
- …
- Counter-Example(s):
- See: Text Item Vectorization Structure, Continuous Distribution Mapping, Document-Wise Co-Occurrence Statistic.