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.

Example Input: The SnapCaptions dataset is composed of 10K user-generated image (snap) and textual caption pairs where named entities in captions are manually labeled by expert human annotators (entity types: PER, LOC, ORG, MISC).
Question: How large is their MNER SnapCaptions dataset?
Example Output: 10K user-generated image (snap) and textual caption pairs

Example Input: We evaluate our model on both English and Chinese segmentation. For both languages we used standard datasets for word segmentation and language modeling. English
The Brent corpus is a standard corpus used in statistical modeling of child language acquisition BIBREF15 , BIBREF16 We use the commonly used version of the PTB prepared by BIBREF17 Since Chinese orthography does not mark spaces between words, there have been a number of efforts to annotate word boundaries. We evaluate against two corpora that have been manually segmented according different segmentation standards. The Beijing University Corpus was one of the corpora used for the International Chinese Word Segmentation Bakeoff BIBREF18 . We use the Penn Chinese Treebank Version 5.1 BIBREF19 .
Question: What dataset is used?
Example Output: Brent corpus PTB Beijing University Corpus Penn Chinese Treebank

Example Input: Only the information concerned with the dictionary definitions are used there, discarding the polarity scores. However, when we utilise the supervised score (+1 or -1), words of opposite polarities (e.g. "happy" and "unhappy") get far away from each other as they are translated across coordinate regions.
Question: How are the supervised scores of the words calculated?
Example Output:
(+1 or -1), words of opposite polarities (e.g. "happy" and "unhappy") get far away from each other