activity
20162020
most cited"President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines

34 citations · 35 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20201 cited

SemEval-2020 Task 7: Assessing Humor in Edited News Headlines

Nabil Hossain, John Krumm, Michael Gamon +1

This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make t…

cs.CL2020

"Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework

Aparna Garimella, Carmen Banea, Nabil Hossain +1

The subjective nature of humor makes computerized humor generation a challenging task. We propose an automatic humor generation framework for filling the blanks in Mad Libs stories…

cs.AI2020

Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines

Nabil Hossain, John Krumm, Tanvir Sajed +1

Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where t…

cs.CL201934 cited

"President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines

Nabil Hossain, John Krumm, Michael Gamon

We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines pa…

cs.AI2016

Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities

Nabil Hossain, Tianran Hu, Roghayeh Feizi +3

Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those…