How is dimensionality defined in a "bag of words" document representation?Number of unique terms in the documentAverage number of words per sentence in the documentTotal number of words in the documentFrequency of repeated words in the document

Given a vocabulary of 500 words, if a document is represented using a Bag of Words (BoW) model, what is the dimensionality of the document vector?Question 28Answera.500b.501c.It depends on the length of the documentd.1000

If a document collection contains 1000 documents and each document is represented using TF-IDF vectors with a vocabulary size of 5000 words, what is the dimensionality of the TF-IDF vectors?Question 7Answera.5000b.1000c.2500d.500

What is a key advantage of word vector embeddings compared to the Bag-of-Words model?AReduced computational complexityBSimplicity and ease of implementationCBetter handling of out-of-vocabulary wordsDAbility to capture semantic relationships between words

Document dimension is also determined by the orientation of the paper.

What is the continuous bag of words (CBOW) approach?1 pointVectors for the neighborhood of words are averaged and used to predict word n.Word n is used to predict the words in the neighborhood of word n.The code for word n is fed through a CNN and categorized with a softmax.Word n is learned from a large corpus of words, which a human has labeled