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

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

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

Question 8What is the Skip-Gram approach?1 pointWord n is learned from a large corpus of words, which a human has labeled.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.Vectors for the neighborhood of words are averaged and used to predict word n.

What is the goal of learning word vectors?1 pointFind the hidden or latent features in a text.Given a word, predict which words are in its vicinity.Labelling a text corpus, so a human doesn’t have to do it.Determine the vocabulary in the codebook.