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: 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?

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

A: Yes
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Q:  To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). To examine the utility of our features in a neural framework, we further adapt our word-level task as a tagging task, and use LSTM as a baseline. Specifically, we concatenate an OP and PC with a special token as the separator so that an LSTM model can potentially distinguish the OP from PC, and then tag each word based on the label of its stemmed version. We use GloVe embeddings to initialize the word embeddings BIBREF40. We concatenate our proposed features of the corresponding stemmed word to the word embedding; the resulting difference in performance between a vanilla LSTM demonstrates the utility of our proposed features. 
 Question: What is the baseline?

A:
random method  LSTM 
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