# Type Hierarchy Enhanced Event Detection without Triggers
###### Abstract  
Event detection (ED) aims to detect events from a given text and categorize them into event types. Most of the current approaches to ED rely heavily on the human annotations of triggers, which are often costly and affect the application of ED in other fields. However, triggers are not necessary for the event detection task. We propose a novel framework called Type Hierarchy Enhanced Event Detection Without Triggers (THEED) to avoid this problem. More specifically, We construct a type hierarchy concept module using the external knowledge graph Probase to enhance the semantic representation of event types. In addition, we divide input instances into word-level and context-level representations, which can make the model use different level features. The experimental result indicates that our proposed approach achieves better improvement. Additionally, it is significantly competitive with mainstream trigger-based models. hierarchy concept; attention mechanism; probase; bi-LSTM; event detection +
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table. Still, the semantic information of event types represented in this way is limited. For each event type, we can find its corresponding superordinate and subordinate concepts to enrich its semantic information, so this paper proposes a type hierarchy concept module to enhance the semantic representation of event types and compensate for some semantic features lost by event triggers. Specifically, it uses the IS_A relationship in the probabilistic knowledge graph Probase [13] to look up the hierarchy concepts of an event type while calculating their confidence based on the frequency of occurrence [14]. Unlike the remote supervision framework [15], our approach does not utilize an external knowledge base to annotate text or generate a new training corpus for event detection. Another limitation is that existing studies consider only context-level features and ignore word-level features in the event detection task [16; 17] or consider every word as a potential trigger when using word-level features [9; 18]. The former will lose an essential part of the features during the event detection task, while the latter will take into account many useless words such as \"a\", \"the\", etc. To this end, we propose a new event detection framework with enhanced type hierarchy called THEED. Our framework represents event types as hierarchy concepts with rich semantics. The hierarchy concepts are then used to complete event detection by leveraging the attention mechanism to capture their interactions with word-level and context-level representations. Specifically, (1) for the type hierarchy concept module, we construct a type hierarchy network by looking for the superordinate and subordinate concepts of each event type using the probabilistic knowledge graph Probase. Furthermore, we calculate  
Figure 1: Event instances from the ACE 2005 benchmark, where the trigger words are colored. Figure 2: Statistics about the number of occurrences of triggers in the ACE2005 dataset. (**a**) Top 10 occurrences of triggers. (**b**) Top 50–60 occurrences of triggers. the confidence of each hierarchy concept to indicate the similarity with the event type. (2) For word-level and context-level representations, our framework first converts the input tokens into embeddings and then applies the bi-LSTM layer to calculate the context-level representation. As for word-level representation, we exploit the attention mechanism to obtain top K contribution to the sentence's semantic information. (3) For event detection, we give an input sentence, and we make the binary decoding of each representation based on the hierarchy concept. Our contributions can be summarized as follows:  
* We propose a trigger-free event detection framework with the semantic enhancement of event types called THEED. * We design a type hierarchy concept enhanced module that uses the Probase probabilistic knowledge graph to construct hierarchical concepts with confidence.