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.

Example Input: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.
Question: Do they perform a quantitative analysis of their model displaying knowledge distortions?
Example Output: Yes

Example Input: In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features.
Question: What baseline method is used?
Example Output: using word2vec to create features that are used as input to the SVM

Example Input: We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries.
Question: What new interesting tasks can be solved based on the uncanny semantic structures of the embedding space?
Example Output:
analogy query analogy browsing