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.

[EX Q]: We cluster the embeddings with INLINEFORM0 -Means. 
[EX A]: How were the cluster extracted? 

[EX Q]: In addition to the majority baseline, we also compare our results with a lexicon-based approach. For experimental results, we report majority baseline for each language where the majority baseline corresponds to a model's accuracy if it always predicts the majority class in the dataset.
[EX A]: what are the baselines?

[EX Q]: Here, we outline the required steps for developing a knowledge graph of interlinked events. Figure FIGREF2 illustrates the high-level overview of the full pipeline. This pipeline contains the following main steps, to be discussed in detail later. (1) Collecting tweets from the stream of several news channels such as BBC and CNN on Twitter. (2) Agreeing upon background data model. (3) Event annotation potentially contains two subtasks (i) event recognition and (ii) event classification. (4) Entity/relation annotation possibly comprises a series of tasks as (i) entity recognition, (ii) entity linking, (iii) entity disambiguation, (iv) semantic role labeling of entities and (v) inferring implicit entities. (5) Interlinking events across time and media. (6) Publishing event knowledge graph based on the best practices of Linked Open Data.
[EX A]:
Which news organisations are the headlines sourced from?