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 measure to compute the
relative polarization of a topical wordset across two distributional
representations. To this end, SWEAT uses two additional wordsets, deemed to
have opposite valence, to represent two different poles. We validate our
approach and illustrate a case study to show the usefulness of the introduced
measure.

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。

SWEAT：跨不同语料库打分话题极化

SWEAT: Scoring Polarization of Topics across Different Corpora

We present the first exploration of meaning shift over short periods of time
in online communities using distributional representations. We create a small
annotated dataset and use it to assess the performance of a standard model for
meaning shift detection on short-term meaning shift. We find that the model has
problems distinguishing meaning shift from referential phenomena, and propose a
measure of contextual variability to remedy this.

本文使用分布式表示探讨网络社区中短期时间内意义转移的现象，创建了一个小的标注数据集，评估了标准模型在短期意义转移检测上的表现，并提出了一种解决模型无法识别联想现象的方法 —— 上下文变异度测量。

短期意义转移：分布式探索

Short-Term Meaning Shift: A Distributional Exploration

Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.

本研究详细介绍了一个基于丰富的单词嵌入的组成分布式框架，旨在促进句子中单词之间的交互，并使用环境中的句法信息来增强向量。研究结果表明，该框架在 MSRPar 任务上取得了具有代表性的结果。