1.What differences would you expect for word embeddings that use a short context window vs. a large context window?

2.What are some problems with count-based methods for representing words?

3.Explain the intuition behind word2vec.

4.Consider the task of learning skip-gram embeddings. Provide 4 positive and 8 negative examples for the target word 'shovel' in the following excerpt: "... I purchased a shovel to rake the leaves in my lawn ..."

5.Consider the weighted unigram frequency formula for negative sampling in word2vec: \(P_\alpha(w) = {{count(w)^\alpha}\over{\sum_{w'} count(w')^\alpha}}\). Why is \(\alpha={3\over4}\) preferred over \(\alpha=1\)?

6.word2vec uses the logistic or sigmoid function to predict if a context word \(c\) is a real context word for a target word \(t\). How can we compute \(P(+| t,c)\)?

7.Compare and contrast CBOW and skip-gram. What are the advantages of each?

8.What's the intuition behind GloVe?

9.How does GloVe handle words that never co-occur together in a training corpus?