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: We focus primarily on the word vector representations (word embeddings) created specifically using the twitter dataset. GloVe BIBREF13 is an unsupervised learning algorithm for obtaining vector representations for words. 200-dimensional GloVe embeddings trained on 2 Billion tweets are integrated. Edinburgh embeddings BIBREF14 are obtained by training skip-gram model on Edinburgh corpus BIBREF15 . Since tweets are abundant with emojis, Emoji embeddings BIBREF16 which are learned from the emoji descriptions have been used. Embeddings for each tweet are obtained by summing up individual word vectors and then dividing by the number of tokens in the tweet.

A: what pretrained word embeddings were used?
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Q: Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances. 

A: Are there any new finding in analasys of trinomials that was not present binomials?
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Q: Crowdsourced annotators assigned similarity to word pairs during the word similarity task. 

A:
did they use a crowdsourcing platform for annotations?
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