The COVID-19 pandemic has changed the research agendas of most scientific
communities, resulting in an overwhelming production of research articles in a
variety of domains, including medicine, virology, epidemiology, economy,
psychology, and so on. Several open-access corpora and literature hubs were
established; among them, the COVID-19 Open Research Dataset (CORD-19) has
systematically gathered scientific contributions for 2.5 years, by collecting
and indexing over one million articles. Here, we present the CORD-19 Topic
Visualizer (CORToViz), a method and associated visualization tool for
inspecting the CORD-19 textual corpus of scientific abstracts. Our method is
based upon a careful selection of up-to-date technologies (including large
language models), resulting in an architecture for clustering articles along
orthogonal dimensions and extraction techniques for temporal topic mining.
Topic inspection is supported by an interactive dashboard, providing fast,
one-click visualization of topic contents as word clouds and topic trends as
time series, equipped with easy-to-drive statistical testing for analyzing the
significance of topic emergence along arbitrarily selected time windows. The
processes of data preparation and results visualization are completely general
and virtually applicable to any corpus of textual documents - thus suited for
effective adaptation to other contexts.

基于 CORD-19 文本摘要的 CORToViz 可视化工具，利用最新科技，通过聚类文章并提取主题的方法，提供快速可视化主题内容和趋势的交互式仪表盘，适用于任何文本文档语料库的数据准备和结果可视化。

探索 COVID-19 大流行期间研究主题的演变

Exploring the evolution of research topics during the COVID-19 pandemic

High-quality phrase representations are essential to finding topics and
related terms in documents (a.k.a. topic mining). Existing phrase
representation learning methods either simply combine unigram representations
in a context-free manner or rely on extensive annotations to learn
context-aware knowledge. In this paper, we propose UCTopic, a novel
unsupervised contrastive learning framework for context-aware phrase
representations and topic mining. UCTopic is pretrained in a large scale to
distinguish if the contexts of two phrase mentions have the same semantics. The
key to pretraining is positive pair construction from our phrase-oriented
assumptions. However, we find traditional in-batch negatives cause performance
decay when finetuning on a dataset with small topic numbers. Hence, we propose
cluster-assisted contrastive learning(CCL) which largely reduces noisy
negatives by selecting negatives from clusters and further improves phrase
representations for topics accordingly. UCTopic outperforms the
state-of-the-art phrase representation model by 38.2% NMI in average on four
entity cluster-ing tasks. Comprehensive evaluation on topic mining shows that
UCTopic can extract coherent and diverse topical phrases.

本文提出了 UCTopic，一种使用无监督对比学习框架来学习上下文感知短语表示和进行主题挖掘，通过正对构建来预训练 UCTopic，使用聚类辅助对比学习 (CCL) 显着降低噪声负例，进一步提高了短语表示的类别学习效果，在实体聚类任务上优于现有的同类模型。