In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB.
[A]: how is model compactness measured?


[Q]: Compared with the BERT-BASE model, BERT-SPC significantly improves the accuracy and F1 score of aspect polarity classification.
[A]: How much better is performance of the proposed model compared to the state of the art in these various experiments?


[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.
[A]:
How do they incorporate sentiment analysis?