Firms perform environmental surveillance to identify important events and their developments. To alleviate the stringent information processing and analysis requirements, automated methods are needed to discover from online news articles distinct episodes (stages) of an important event. We propose a time-adjoining frequent itemset-based method that incorporates essential temporal characteristics of news articles for event episode discovery. With a corpus of 1468 news articles that pertain to 248 episodes of 53 different events, we empirically evaluate the proposed method and include several prevalent techniques as benchmarks. The results show that our method outperforms the benchmark techniques consistently and significantly, attaining the cluster recall, cluster precision, and F-measure values at 0.706, 0.593, and 0.584, respectively.

公司执行环境监视以识别重要事件及其发展。为了减轻严格的信息处理和分析要求，需要一种自动方法来从在线新闻文章中发现重要事件的不同片段（阶段）。我们提出了一种基于时间的，基于频繁项集的方法，该方法结合了新闻文章的基本时态特征以进行事件集发现。我们拥有1468篇新闻文章的语料库，涉及53个不同事件的248集，我们根据经验对所提出的方法进行评估，并包括几种流行的技术作为基准。结果表明，我们的方法始终如一且显着优于基准技术，分别达到了0.706、0.593和0.584的聚类召回率，聚类精度和F度量值。