Humor is an important social phenomenon, serving complex social and
psychological functions. However, despite being studied for millennia humor is
computationally not well understood, often considered an AI-complete problem.
In this work, we introduce a novel setting in humor mining: automatically
detecting funny and unusual scientific papers. We are inspired by the Ig Nobel
prize, a satirical prize awarded annually to celebrate funny scientific
achievements (example past winner: "Are cows more likely to lie down the longer
they stand?"). This challenging task has unique characteristics that make it
particularly suitable for automatic learning. We construct a dataset containing
thousands of funny papers and use it to learn classifiers, combining findings
from psychology and linguistics with recent advances in NLP. We use our models
to identify potentially funny papers in a large dataset of over 630,000
articles. The results demonstrate the potential of our methods, and more
broadly the utility of integrating state-of-the-art NLP methods with insights
from more traditional disciplines.

本研究提出了一个基于机器学习的新型幽默挖掘方法，通过结合心理学、语言学与自然语言处理的最新进展，利用一万余篇滑稽论文构建数据集，成功实现自动检测幽默科学论文的任务，并有望在科学研究中发挥重要作用。