吴恩达深度学习学习笔记——C5W2——自然语言处理与词嵌入——练习题_natural language processing & word embeddings答案

C5W2 Quiz - Natural Language Processing & Word Embeddings

20230704 updated:

 

 

 

 

Ans: False

Ans: B

Ans: True

Ans: A、C

Ans: A

Ans: True

Ans: A

Ans: A、C

Ans: B、C、D

Ans: A

1. Suppose you learn a word embedding for a vocabulary of 10000 words. Then the embedding vectors should be 10000 dimensional, so as to capture the full range of variation and meaning in those words.

Ans: False

2. What is t-SNE?

Ans: A non-linear dimensionality reduction technique

3. Suppose you download a pre-trained word embedding which has been trained on a huge corpus of text. You then use this word embedding to train an RNN for a language task of recognizing if someone is happy from a short snippet of text, using a small training set.

x (input text)    y (happy?)

I'm feeling wonderful today! 1

I'm bummed my cat is ill.         0

Really enjoying this!         1

Then even if the word “ecstatic” does not appear in your small training set, your RNN might reasonably be expected to recognize “I’m ecstatic” as deserving a label y = 1y=1.

Ans: True

4. Which of these equations do you think should hold for a good word embedding? (Check all that apply)

Ans: e_{boy} - e_{girl} ≈ e_{brother} - e_{sister}

           e_{boy} - e_{brother} ≈ e_{girl} - e_{sister}

5. Let E be an embedding matrix, and let o1234 be a one-hot vector corresponding to word 1234. Then to get the embedding of word 1234, why don’t we call E∗o1234 in Python?

Ans: It is computationally wasteful.

6. When learning word embeddings, we create an artificial task of estimating P(target \mid context)P(target∣context). It is okay if we do poorly on this artificial prediction task; the more important by-product of this task is that we learn a useful set of word embeddings.

Ans: False

7. In the word2vec algorithm, you estimate P(t \mid c)P(t∣c), where t is the target word and c is a context word. How are t and c chosen from the training set? Pick the best answer.

Ans: c and t are chosen to be nearby words.

8. Suppose you have a 10000 word vocabulary, and are learning 500-dimensional word embeddings. The word2vec model uses the following softmax function:

P(t∣c)=eθTtec∑10000t′=1eθTt′ec

Which of these statements are correct? Check all that apply.

Ans: θt and ec are both 500 dimensional vectors.

            θt and ec are both trained with an optimization algorithm such as Adam or gradient descent.

9. Suppose you have a 10000 word vocabulary, and are learning 500-dimensional word embeddings.The GloVe model minimizes this objective:

min∑10,000i=1∑10,000j=1f(Xij)(θTiej+bi+b′j−logXij)2

Which of these statements are correct? Check all that apply.

Ans: θi and ej hould be initialized to 0 at the beginning of training.

         Xij is the number of times word i appears in the context of word j.

         The weighting function f(.)f(.) must satisfy f(0) = 0f(0)=0.

10. You have trained word embeddings using a text dataset of m1 words. You are considering using these word embeddings for a language task, for which you have a separate labeled dataset of m2 words. Keeping in mind that using word embeddings is a form of transfer learning, under which of these circumstance would you expect the word embeddings to be helpful?

Ans: m1 >> m2