Automated event detection from news corpora is a crucial task towards mining
fast-evolving structured knowledge. As real-world events have different
granularities, from the top-level themes to key events and then to event
mentions corresponding to concrete actions, there are generally two lines of
research: (1) theme detection identifies from a news corpus major themes (e.g.,
"2019 Hong Kong Protests" vs. "2020 U.S. Presidential Election") that have very
distinct semantics; and (2) action extraction extracts from one document
mention-level actions (e.g., "the police hit the left arm of the protester")
that are too fine-grained for comprehending the event. In this paper, we
propose a new task, key event detection at the intermediate level, aiming to
detect from a news corpus key events (e.g., "HK Airport Protest on Aug.
12-14"), each happening at a particular time/location and focusing on the same
topic. This task can bridge event understanding and structuring and is
inherently challenging because of the thematic and temporal closeness of key
events and the scarcity of labeled data due to the fast-evolving nature of news
articles. To address these challenges, we develop an unsupervised key event
detection framework, EvMine, that (1) extracts temporally frequent peak phrases
using a novel ttf-itf score, (2) merges peak phrases into event-indicative
feature sets by detecting communities from our designed peak phrase graph that
captures document co-occurrences, semantic similarities, and temporal closeness
signals, and (3) iteratively retrieves documents related to each key event by
training a classifier with automatically generated pseudo labels from the
event-indicative feature sets and refining the detected key events using the
retrieved documents. Extensive experiments and case studies show EvMine
outperforms all the baseline methods and its ablations on two real-world news
corpora.

本文提出了一种新的任务，中间级别的重要事件检测，旨在从新闻语料库中检测在特定时间 / 位置发生的关键事件，并聚焦于相同的主题。为了解决这些挑战，我们开发了一个无监督的重要事件检测框架 EvMine，它提取时间频繁的 peak 短语，通过从我们设计的 peak 短语图中检测社区将 peak 短语合并成事件相关的特征集，使用从事件相关特征集自动生成的伪标签训练分类器，并利用检索到的文档迭代检测到的关键事件。

大规模文本语料中的无监督关键事件检测

Unsupervised Key Event Detection from Massive Text Corpora

In this paper, we develop the continuous time dynamic topic model (cDTM). The
cDTM is a dynamic topic model that uses Brownian motion to model the latent
topics through a sequential collection of documents, where a "topic" is a
pattern of word use that we expect to evolve over the course of the collection.
We derive an efficient variational approximate inference algorithm that takes
advantage of the sparsity of observations in text, a property that lets us
easily handle many time points. In contrast to the cDTM, the original
discrete-time dynamic topic model (dDTM) requires that time be discretized.
Moreover, the complexity of variational inference for the dDTM grows quickly as
time granularity increases, a drawback which limits fine-grained
discretization. We demonstrate the cDTM on two news corpora, reporting both
predictive perplexity and the novel task of time stamp prediction.

本文介绍了一种新的连续时间动态主题模型（cDTM），它使用布朗运动模拟文档序列中的潜在主题，通过稀疏观测值使用变分推理算法进行有效推断。我们在两个新闻语料库上测试了该模型，并通过预测困惑度和时间戳预测等指标进行了评估。