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.
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Question: These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. 
 Question: How do they extract topics?

Answer:  automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15


Question: Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets.  Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items.  
 Question: Do they report results only on English data?

Answer: Yes


Question: The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page (for PDFs only) was specified as attributes to the token elements of the tokens layer (cf. Figure FIGREF34 for an example) 
 Question: Which information about typography is included in the corpus?

Answer:
font type font style Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page