Deep Learning IIT Ropar Week 9 Nptel Assignment Answers
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Let (X) be the co-occurrence matrix such that the ((i,j))-th entry of (X) captures the PMI between the (i)-th and (j)-th word in the corpus. Every row of (X) corresponds to the representation of the (i)-th word in the corpus. Suppose each row of (X) is normalized (i.e., the (L2) norm of each row is 1). Then the ((i,j))-th entry of (XX^T) captures the: A) PMI between word (i) and word (j) B) Euclidean distance between word (i) and word (j) C) Probability that word (i) D) Cosine similarity between word (i)
Answer:D) Cosine similarity between word (i)
Consider the following corpus: “human machine interface for computer applications. user opinion of computer system response time. user interface management system. system engineering for improved response time”. What is the size of the vocabulary of the above corpus? A) 13 B) 14 C) 15 D) 16
Answer: Updating soon in Progress
At the input layer of a continuous bag of words model, we multiply a one-hot vector (x \in R^{|V|}) with the parameter matrix (W \in R^{k \times |V|}). What does each column of (W) correspond to? A) The representation of the (i)-th word in the vocabulary B) The (i)-th eigenvector of the co-occurrence matrix
Answer:A) The representation of the (i)-th word in the vocabulary
You are given the one-hot representation of two words below: (\text{CAR} = [1,0,0,0,0]), (\text{BUS} = [0,0,0,1,0]). What is the Euclidean distance between CAR and BUS? A) 1 point
Answer: Updating soon in Progress
Let (\text{count}(w, c)) be the number of times the words (w) and (c) appear together in the corpus (i.e., occur within a window of few words around each other). Further, let (\text{count}(w)) and (\text{count}(c)) be the total number of times the word (w) and (c) appear in the corpus respectively and let (N) be the total number of words in the corpus. The PMI between (w) and (c) is then given by: A) (\log \frac{\text{count}(w, c) \times \text{count}(w)}{N \times \text{count}(c)}) B) (\log \frac{\text{count}(w, c) \times \text{count}(c)}{N \times \text{count}(w)}) C) (\log \frac{\text{count}(w, c) \times N}{\text{count}(w) \times \text{count}(c)})
Answer:B) (\log \frac{\text{count}(w, c) \times \text{count}(c)}{N \times \text{count}(w)})
These are Deep Learning IIT Ropar Week 9 Nptel Assignment Answers
Which of the following is true about the input representation in the CBOW model? A) Each word is represented as a one-hot vector B) Each word is represented as a continuous vector C) Each word is represented as a sequence of one-hot vectors D) Each word is represented as a sequence of continuous vectors
Answer: Updating soon in Progress
Which of the following is an advantage of using the skip-gram method over the bag-of-words approach? A) The skip-gram method is faster to train B) The skip-gram method performs better on rare words C) The bag-of-words approach is more accurate D) The bag-of-words approach is better for short texts
What is the role of the softmax function in the skip-gram method? A) To calculate the dot product between the target word and the context words B) To transform the dot product into a probability distribution C) To calculate the distance between the target word and the context words D) To adjust the weights of the neural network during training
Answer: Updating soon in Progress
We add incorrect pairs into our corpus to maximize the probability of words that occur in the same context and minimize the probability of words that occur in different contexts. This technique is called- A) Hierarchical softmax B) Contrastive estimation C) Negative sampling D) Glove representations
Answer: Updating soon in Progress
What is the computational complexity of computing the softmax function in the output layer of a neural network? A) (O(n)) B) (O(n^2)) C) (O(n\log n)) D) (O(\log n))
Answer: Updating soon in Progress
These are Deep Learning IIT Ropar Week 9 Nptel Assignment Answers
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