Federico Bianchi, Marco Marelli, Paolo Nicoli, Matteo Palmonari
TL;DR本文提出了“Sliced Word Embedding Association Test”（SWEAT）这一新的统计量，以计算两个分布表征中一组主题词的相对极性，用于理解语料库中观点的差异，通过使用两个相反极性的额外词组作为极点，验证了我们的方法并且阐述了引入该测量的实用性。
Abstract
Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the sliced word embedding association test (SWEAT), a novel statistical me