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.

[EX Q]: Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march are crucial factors in conveying bias. 
 Question: What factors contribute to interpretive biases according to this research?
[EX A]: Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march

[EX Q]: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. 
 Question: What are the baselines?
[EX A]: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6

[EX Q]: These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient’s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain.

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. 45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. 
 Question: How do they incorporate sentiment analysis?
[EX A]:
features per admission were extracted as inputs to the readmission risk classifier