Abstract: Event detection (ED) is a fundamental task of event extraction, which aims to detect triggers in text and determine their event types. Most existing methods regard event detection as a sentence-level classification problem, ignoring the correlations between events in different sentences. A novel event detection framework, named document embedding networks combined with semantic space (DENSS), is proposed in this paper. The document-level information is utilized to alleviate semantic ambiguity and enhance contextual understanding. Specifically, the representations of event types and triggers are obtained through an off-the-shelf pre-trained model and a designed multi-level attention mechanism. Then the feature vectors of event types and triggers are mapped into a shared semantic space, where the distance represents the correlation of different events. The experimental results on the benchmark dataset demonstrate that our method outperforms most existing methods, and justify the effectiveness of document-level information with shared semantic space.