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]: Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories.
Question: Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people?
[EX A]: No

[EX Q]: Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0

where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 .
Question: Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
[EX A]: monte-carlo sequential sampling

[EX Q]: We use two datasets for experiments (Table TABREF29 ): (a) STAN INLINEFORM0 , created by BIBREF10 BansalBV15, which consists of 1,108 unique English hashtags from 1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36 along with their crowdsourced segmentations and our additional corrections; and (b) STAN INLINEFORM1 , our new expert curated dataset, which includes all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset.
Question: How is the dataset of hashtags sourced?
[EX A]:
1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36 all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset