Dynamic topic models track the evolution of topics in sequential documents,
which have derived various applications like trend analysis and opinion mining.
However, existing models suffer from repetitive topic and unassociated topic
issues, failing to reveal the evolution and hindering further applications. To
address these issues, we break the tradition of simply chaining topics in
existing work and propose a novel neural \modelfullname. We introduce a new
evolution-tracking contrastive learning method that builds the similarity
relations among dynamic topics. This not only tracks topic evolution but also
maintains topic diversity, mitigating the repetitive topic issue. To avoid
unassociated topics, we further present an unassociated word exclusion method
that consistently excludes unassociated words from discovered topics. Extensive
experiments demonstrate our model significantly outperforms state-of-the-art
baselines, tracking topic evolution with high-quality topics, showing better
performance on downstream tasks, and remaining robust to the hyperparameter for
evolution intensities. Our code is available at this https URL .

提出了一种新的神经模型来解决现有动态主题模型中的重复主题和不相关主题问题，并通过引入新的进化跟踪对比学习方法和不相关词排除方法来提高主题进化的质量和多样性。

演化跟踪对比学习和无关词排除在链式自由时尚中建模动态主题

Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking  Contrastive Learning and Unassociated Word Exclusion

This paper presents ATEM, a novel framework for studying topic evolution in
scientific archives. ATEM is based on dynamic topic modeling and dynamic graph
embedding techniques that explore the dynamics of content and citations of
documents within a scientific corpus. ATEM explores a new notion of contextual
emergence for the discovery of emerging interdisciplinary research topics based
on the dynamics of citation links in topic clusters. Our experiments show that
ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP
archive of over five million computer science articles.

ATEM 是一种新型框架，基于动态主题建模和动态图嵌入技术，探索科技文献库中文献内容和引用的动态变化，发现新兴的跨学科研究主题，实验结果表明，ATEM 可以有效地检测计算机科学文章数据库中的新兴跨学科主题。

ATEM: 一种用于检测科学档案中新兴主题的主题演化模型

ATEM: A Topic Evolution Model for the Detection of Emerging Topics in  Scientific Archives

Recent extensively competitive business environment makes companies to keep
their eyes on social media, as there is a growing recognition over customer
languages (e.g., needs, interests, and complaints) as source of future
opportunities. This research avenue analysing social media data has received
much attention in academia, but their utilities are limited as most of methods
provide retrospective results. Moreover, the increasing number of
customer-generated contents and rapidly varying topics have made the necessity
of time-aware topic evolution analyses. Recently, several researchers have
showed the applicability of pre-trained semantic language models to social
media as an input feature, but leaving limitations in understanding evolving
topics. In this study, we propose a time-aware topic identification approach
with pre-trained language models. The proposed approach consists of two stages:
the dynamics-focused function for tracking time-varying topics with language
models and the emergence-scoring function to examine future promising topics.
Here we apply the proposed approach to reddit data on electric vehicles, and
our findings highlight the feasibility of capturing emerging customer topics
from voluminous social media in a time-aware manner.

本研究提出了一种基于预训练语言模型的时序话题识别方法，采用 reddit 电动汽车数据进行实证分析，证明了从海量社交媒体中以时序方式捕获新出现话题的可行性。