# Type Hierarchy Enhanced Event Detection without Triggers
## 3 Methodology
### Event Detection  
Given an input sentence, we aim to detect the corresponding event type automatically. In order to achieve this goal, we should capture the semantic correlation of instance encoding to the event type. Thus, we adopt an attention mechanism to learn a weight distribution over the two-level representation of the event type and obtain an event type \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Event Type** &
\begin{tabular}{c} **Superordinate** \\ **Concept** \\ \end{tabular} & **Confidence** & **Subordinate Concept** & **Confidence** \\ \hline \multirow{4}{*}{Arrest-Jail} & legal problem & 0.15157 & los angeles county jail & 0.09714 \\  & terminal event & 0.13662 & rape suspect & 0.08928 \\ \cline{1-1}  & punishment & 0.07898 & county jail & 0.07142 \\ \cline{1-1}  &... &... &... &... \\ \cline{1-1}  & public facility & 0.07590 & east cambridge jail & 0.05357 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hierarchical concept and confidence for an example of “Arrest-Jail”; due to space constraints, we only display part of the hierarchical concept. aware two-level representation, where the two-level representation refers to context-level representation and word-level representation. \[A_{word}=\alpha^{\mathrm{T}}\cdot h_{word} \tag{12}\]  
\[\alpha^{k}=\frac{\exp(h_{k}\cdot t^{\mathrm{T}})}{\sum_{i}^{K}\exp(h_{i}\cdot t ^{\mathrm{T}})} \tag{13}\]  
\[A_{context}=h_{context}\cdot t^{\mathrm{T}} \tag{14}\]  
where \(\alpha=[\alpha_{1},\alpha_{2}\cdots\alpha_{k}]\) is the attention vector, and \(A_{word}\) denotes the event type aware word-level representation. Similarly, we use \(A_{context}\) to indicate the event type aware context-level representation. The semantic relevance captured using only the event type provided by ACE205 is limited, so we use the type-hierarchy-enhanced concepts to complete event detection without triggers after obtaining the word-level and context-level type aware representations. \[s_{word}^{sup}=\sigma(A_{word}WV_{sup}) \tag{15}\]  
\[y_{word}^{sup}=\sum s_{word}^{sup}SupScore \tag{16}\]  
Wherein \(W\) is a learnable parameter matrix, \(V_{sup}\) is a superordinate concept representation matrix and \(\sigma\) is the Sigmoid function. \(y_{word}^{sup}\) is one of the outputs, which is designed to capture the feature between word-level representation and the superordinate concept. Similarly, due to the space limitations, we use \(y_{word}^{sub}\), \(y_{context}^{sup}\) and \(y_{context}^{sub}\) to denote the results of the other three levels. Finally, \(y\) is defined as the weighted sum of four outputs. \[y=\lambda(y_{word}^{sup}+y_{word}^{sub})+(1-\lambda)(y_{context}^{sup}+y_{context }^{sub}) \tag{17}\]  
Here, \(\lambda\in[0,1]\) is a hyperparameter for trade-off between word-level outputs and context-level outputs. To determine whether the input sentence contains a certain event type, we follow the settings in [10] as the following equation, where the number \(1\) indicates that the sentence includes an event and otherwise denotes the label of \"NA\". \[\widetilde{y}= \begin{cases}0&y<0.5\\ 1&\text{otherwise}\end{cases}  \tag{18}\]  
In order to solve the multilabel problem in the input instance, our framework converts multilabel classification into binary decoding. As shown in Table 2, each training sample is a \(<\)sentence instance, event type\(>\) pair. Assume that the sentence has three predefined event types \(t1\), \(t2\), \(t3\), and \(t1\), \(t3\) are correct. Then, the label is \(1\), \(0\), \(1\). After analyzing the training corpus, we find that the quantity of positive samples is much less than negative samples, and in general, positive samples will contain more information. So, we use a bias term to strengthen the positive samples [42]. The training objective is to minimize the following loss function:  
\[\mathcal{L}=\frac{1}{T}\sum_{i=1}^{T}{(y(x^{i})-\widetilde{y}^{i})^{2}(1+ \widetilde{y}^{i}\cdot\beta)}+\delta||\theta||^{2} \tag{19}\]