新聞媒體對於民眾參與政府公共政策具有相當的影響力。隨著移動設備和移動互聯網的普及，新聞標題已成為吸引讀者注意力的主要方式之一，越來越多社群媒體網站與媒體提供商（例如 CNN、BBC等）每日發布海量的新聞標題。這些新聞標題可能涵蓋不同的主題，包含噪音或重疊的內容。這使讀者越來越難以快速獲得感興趣的新聞資訊，也使政府難快速掌握民眾關切的新聞事件。為減少新聞噪音或單詞之間的相關性對對於事件檢測的影響，本研究提出一種基於詞嵌入技術的金字塔關係算法（Pyramid relation algorithm, PRA），加強單詞之間的關係找到事件的特徵，減少新聞噪聲的影響，提升新聞事件檢測準確性。此外，本研究採用2019年COVID-19大流行期間的新聞數據集來檢測新聞子事件，例如：醫療用品短缺、經濟措施與防疫措施等。然而，子事件檢測比事件檢測困難，因為每個子事件與主要事件共存著相同或相似的文字特徵。因此，本研究提出一種基於集成分群技術的方法，結合BERT句子嵌入模型和兩階段濾濾機制，改善一般事件檢測方法無法有效地檢測子事件的問題。
本文使用真實世界的新聞數據集，與本研究提出的PRA事件檢測及子事件檢測分別進行實驗。實驗結果表明，PRA觸發詞提取方法有助於提升獲得良好的事件性能。與其他事件檢測方法相比，基於PRA的新聞事件檢測方法的衡量指標性能提高達22%。此外，本研究所提的子事件檢測方法，與其他模型比較表明，效能提升可達81%。實驗結果顯示本研究提出的兩項事件檢測方法優於其他方法，且有效快速地找出新聞事件。這研究不但可減少政府機關花費大量時間彙集新聞事件，也朝向新聞事件分析細緻化的方向發展，邁向智能新聞系統的關鍵一步。

The news media has significant influence on people's participation in the government's public policy. With the popularity of mobile devices and mobile Internet, news headlines have become one of the main ways to attract readers' attention. More and more social media sites and news media providers (such as CNN, BBC, etc.) publish a large number of news headlines every day. These headlines may cover different topics, contain noise or overlap. It was becoming more and more difficult for readers to quickly obtain interesting news information, and it was difficult for the government to quickly grasp news events of public concern. In order to reduce the impact of news noise or correlation between words on event detection, this study proposes a Pyramid Relational Algorithm (PRA) based on word embedding technology to strengthen the relationship between words to find the features of events. In addition, this study used the news dataset during the 2019 COVID-19 pandemic to detect news sub-events, such as: shortage of medical supplies, economic measures and epidemic prevention measures, etc. However, sub-event detection was more difficult than event detection because each sub-event coexisted with the main event with the same or similar text features. Therefore, this study proposed a method based on ensemble clustering technology, which combined sentence embedding model and two-stage filtering mechanism to improve the problem that general event detection methods cannot effectively detect sub-events.
This paper used real-world news datasets to conduct experiments with the PRA event detection and sub-event detection proposed in this study, respectively. The experimental results showed that the PRA trigger word extraction method help to improve the performance of events. Compared with other event detection methods, the metric performance of the PRA-based news event detection method was improved by up to 22%. In addition, the sub-event detection method proposed in this study, compared with other models, shows that the performance can be improved by up to 81%. The two event detection methods proposed in this study outperform other methods and were effective and fast in finding news events. This research can not only reduce the time spent by government agencies in gathering news events, but also develops in the direction of detailed analysis of news events.

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