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

What do N-gram models represent in natural language processing? Question 6Answera.A model that captures the context of words in a sentenceb.A model that represents text as a set of unique words with their respective counts, considering sequences of n wordsc.A model that identifies and classifies named entities in textd.A model that predicts the next word in a sequence of text

What is the function of a language model in NLP?*1 pointTo classify text into predefined categoriesTo generate the probability of a sequence of wordsTo translate text from one language to anotherTo identify parts of speech in a sentence

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 is NOT a characteristic of language models?<br /> A. a. They predict the next word in a sequence. <br />B. b. They determine sentence probability. <br />C. c. They transform text into speech. <br />D. d. They handle unknown words.

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