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.

Q: Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. 
 Question: what types of features were used?

A: stylometric, lexical, grammatical, and semantic
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Q: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?

A: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same
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Q: We initialized the embeddings of these words with 300 dimensional Glove embeddings BIBREF31 .  
 Question: Do they use pretrained embeddings?

A:
Yes
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