Tamer Elsayed

TL;DR: This paper presents a MapReduce algorithm for computing pairwise document similarity in large document collections that exhibits linear growth in running time and space in terms of the number of documents.

TL;DR: The CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims as mentioned in this paper focused on the check-worthiness task, where the task asks to predict which claims in a political debate should be prioritized for fact-checking.

TL;DR: Cost-benefit analysis over 10,000 judgments collected on Mechanical Turk suggests a win-win: experienced crowd workers provide rationales with almost no increase in task completion time while providing a multitude of further benefits, including more reliable judgments and greater transparency for evaluating both human raters and their judgments.

TL;DR: The CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims as mentioned in this paper was the first edition of the CLEF task, which focused on predicting which potential claims in a political debate should be prioritized for fact-checking; in particular, given a debate or a political speech, the goal was to produce a ranked list of sentences based on their worthiness for fact checking.

TL;DR: Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average.