{"description":"Test your understanding of how words are represented as vectors in NLP and how embeddings capture semantic relationships.","questions":[{"answer":"To convert words into numerical vectors for machine learning","number":1,"options":["To translate audio into text","To generate HTML markup","To convert words into numerical vectors for machine learning","To display font styles in documents"],"question":"What is the purpose of word embeddings in NLP?"},{"answer":"In close proximity to each other","number":2,"options":["With very different vector lengths","In close proximity to each other","Using the same vector","In reverse alphabetical order"],"question":"How are similar words typically represented in embedding space?"},{"answer":"A latent linguistic attribute","number":3,"options":["A specific sentence","A document type","A latent linguistic attribute","A frequency of punctuation"],"question":"What does each dimension in a word embedding vector typically represent?"},{"answer":"BERT","number":4,"options":["TF-IDF","Word2Vec","BERT","Bag-of-Words"],"question":"Which model provides context-sensitive embeddings?"},{"answer":"Contextual embeddings vary depending on word usage in context","number":5,"options":["Contextual embeddings use image data","Traditional embeddings change with each sentence","Contextual embeddings vary depending on word usage in context","Traditional embeddings require no training"],"question":"What is a key difference between traditional and contextual word embeddings?"},{"answer":"Distributed word embedding","number":6,"options":["One-hot encoding","TF-IDF vector","Distributed word embedding","Character-level count"],"question":"Which type of embedding would most likely capture the difference between 'king' and 'queen'?"},{"answer":"Words share similar semantic or grammatical features","number":7,"options":["Words have the same length","Words share similar semantic or grammatical features","Words are rarely used","Words were generated synthetically"],"question":"In embedding visualizations, what does clustering typically indicate?"},{"answer":"They capture more semantic detail and word relationships","number":8,"options":["They reduce training time","They simplify network architectures","They capture more semantic detail and word relationships","They generate more documents"],"question":"Why are high-dimensional embeddings often used in practice?"},{"answer":"'King' - 'Man' + 'Woman' = 'Queen'","number":9,"options":["'King' - 'Man' + 'Woman' = 'Queen'","'Red' + 'Yellow' = 'Orange'","'Dog' + 'Tree' = 'Forest'","'Word' * 'Vector' = 'Matrix'"],"question":"What common analogy demonstrates the power of word embeddings?"},{"answer":"Sentiment analysis","number":10,"options":["Audio transcription","Image classification","Sentiment analysis","Video rendering"],"question":"Which of the following is an application that relies on word embeddings?"}],"title":"Word Embedding"}