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

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

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

Which of the following represents the Bag of Words (BoW) model in natural language processing?Question 8Answera.A model that represents text as a set of unique words with their respective counts, ignoring grammar and word orderb.A model that represents text as a sequence of word embeddingsc.A model that predicts the next word in a sequence of textd.A model that captures the context of words in a sentence

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