NLP. Quiz 1
Intro to NLP and Deep Learning. Word embeddings.

Some questions can be not mentioned in the lecture explicitly, but you can still use logic and google.
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What are the advantages of deep learning approach over classical machine learning approach?
2 points
Should one have domain specific knowledge in, say pharmacology, to predict possible drugs using deep learning against given disease?
1 point
Clear selection
What is the main difficulty of processing natural language?
1 point
Clear selection
How many verbs in the sentence: "Can you can a can as a canner can can a can?"
Clear selection
What is the most possible solution of equation: word2vec('"king") + word2vec("woman") - word2vec("man") = x?
2 points
Let the vector representation for the word "jungle" be [-0.123 0.432 1.453 -0.003]. Which of these vectors are probable to be representations of the word "forest"?
1 point
What are the advantages of using small dense vector representations (eg. word2vec) compared to large sparse vectors (eg. TF-IDF)
2 points
Check all true statements about Negative Sampling
2 points
Which of the following tasks can be used for *intrinsic* word vector evaluation?
2 points
Your questions about the lecture (if any, you may write in Russian as well)
1 point
Any suggestions how to make this course better
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