Bag of Words
Bag-of-words represents a document as an unordered count vector of how many times each vocabulary word appears, discarding word order and grammar entirely.
What is Bag of Words?
Bag-of-words represents a document as an unordered count vector of how many times each vocabulary word appears, discarding word order and grammar entirely.
Bag-of-words represents a document as an unordered count vector of how many times each vocabulary word appears, discarding word order and grammar entirely.
Where is it used?
BoW is used in spam classifiers, sentiment baselines, and topic-modeling (LDA); it is too lossy for LLMs that depend on token order, but still serves as a baseline in NLP courses.
How to build it
Use `sklearn.feature_extraction.text.CountVectorizer` on a list of sentences, inspect `.toarray()`, and build a Naive Bayes classifier to see how far orderless features get.