In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Input: Consider Input: Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. 
 Question: what levels of document preprocessing are looked at?

Output: raw text text cleaning through document logical structure detection removal of keyphrase sparse sections of the document


Input: Consider Input: As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (42–49 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape. 
 Question: What is proof that proposed functional form approximates well generalization error in practice?

Output: estimated test accuracy is highly correlated with actual test accuracy for various datasets appropriateness of the proposed function for modeling the complex error landscape


Input: Consider Input: Conversation Based Features
These features are devoted to exploit the peculiar characteristics of the dataset, which have a tree structure reflecting the conversation thread.

Text Similarity to Source Tweet: Jaccard Similarity of each tweet with its source tweet.

Text Similarity to Replied Tweet: the degree of similarity between the tweet with the previous tweet in the thread (the tweet is a reply to that tweet).

Tweet Depth: the depth value is obtained by counting the node from sources (roots) to each tweet in their hierarchy. 
 Question: What conversation-based features are used?


Output: Text Similarity to Source Tweet Text Similarity to Replied Tweet Tweet Depth


