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.
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Question: We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.
Question: How is the dataset collected?

Answer: A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al HEOT obtained from one of the past studies done by Mathur et al

Question: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles).
Question: what lexical features did they experiment with?

Answer: TF.IDF-based features

Question: Specifically, we group the models based on the objective function it optimizes. We believe this work can aid the understanding of the existing literature. We believe that the performance of these models is highly dependent on the objective function it optimizes predicting adjacent word (within-tweet relationships), adjacent tweet (inter-tweet relationships), the tweet itself (autoencoder), modeling from structured resources like paraphrase databases and weak supervision.
Question: How do they encourage understanding of literature as part of their objective function?

Answer:
They group the existing works in terms of the objective function they optimize - within-tweet relationships, inter-tweet relationships, autoencoder, and weak supervision.