# Deliverable 1.3

Why do you think the type-token ratio is lower for the dev data as compared to the training data?

(Yes the dev set is smaller; why does this impact the type-token ratio?)


# Deliverable 3.5

Explain what you see in the scatter plot of weights across different smoothing values.

# Deliverable 6.2

Now compare the top 5 features for logistic regression under the largest regularizer and the smallest regularizer.
Paste the output into ```text_answers.md```, and explain the difference. (.4/.2 points)


# Deliverable 7.2

Explain the new preprocessing that you designed: why you thought it would help, and whether it did.

# Deliverable 8

Describe the research paper that you have chosen.

- What are the labels, and how were they obtained?
- Why is it interesting/useful to predict these labels?  
- What classifier(s) do they use, and the reasons behind their choice? Do they use linear classifiers like the ones in this problem set?
- What features do they use? Explain any features outside the bag-of-words model, and why they used them.
- What is the conclusion of the paper? Do they compare between classifiers, between feature sets, or on some other dimension? 
- Give a one-sentence summary of the message that they are trying to leave for the reader.