# LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
## 3 Materials and Methods
### Event Argument Extraction
In the \(t\)th time step, the hidden layer state of BiLSTM output is \(\mathbf{h}_{ni}^{t}\) with the input \(\mathbf{\hat{e}}_{ni}^{t}\); this process is as following:  
\[\left[\mathbf{h}_{n1}^{L}\right],\ \left[\mathbf{h}_{n2}^{L}\right],\cdots,\ \left[\mathbf{h}_{ni }^{L}\right]=BiLSTM\left(\left[\mathbf{\hat{e}}_{n1}^{L}],[\mathbf{\hat{e}}_{n2}^{L}], \cdots,[\mathbf{\hat{e}}_{ni}^{L}]\right) \tag{5}\]  
The dropout layer is adopted to make some bi-directional long short-term memory units randomly inactivated for avoiding over-fitting of training results, and the semantic information of short word-level is enhanced by concatenating the vectors fused semantic features. \[F_{2}\left(\mathbf{h}_{ni}^{L},\mathbf{h}_{ni}^{L},\mathbf{\hat{e}}_{ni}^{L}\right)=f \left(\mathbf{W}^{L}\mathbf{r}^{L}*\left(\mathbf{h}_{ni}^{L}\parallel\mathbf{\hat{e}}_{ni}^{L }\right)+b^{L}\right) \tag{6}\]  
where \(\mathbf{W}^{L}\in\mathbb{R}^{d_{nsi}\times d_{\delta}}\) is the weight matrix, \(b^{L}\) is bias, and \(f(*)\) is activation function, \(\mathbf{r}^{L}=\left[r_{n1}^{L},\cdots,r_{ni}^{L}\right],r_{ni}^{L}\sim\text{ Bernoulli}(p)\). Here, Bernoulli is utilized to randomly generate a vector of 0 or 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relation Types** & **Labels** & **Relation Types** & **Labels** \\ \hline Subject–predicate relation & SBV & Verb–complement construction & CMP \\ Verb–object relation & VOB & Juxtaposition & COO \\ Indirect object relation & IOB & Preposition-object relation & POB \\ Preposed–object & FOB & Left additional relation & LAD \\ Concurrent remark & DBL & Right additional relation & RAD \\ Modifier–head construction & ATT & Independent structure & IS \\ Mesomorphic construction & ADV & Punctuation & W \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Relations under dependency syntactic. Figure 3: Recognition model for short word-level by fusing semantic feature vectors. Finally, the output \(\hat{h}_{ni}^{L}\) of dropout is entered into CRF and the corresponding label sequence is \(y^{a}=(y_{n1}^{a},\cdots,y_{ni}^{a})\). Then, all parameters of CRF can be estimated by maximizing \(P(y^{a}|\hat{h}^{L})\) for the given HPI text \(R\):  
\[P(y^{a}|\hat{h}^{L})=\frac{1}{Z\Big{(}\hat{h}_{ni}^{L}\Big{)}}\text{exp}\, \left(\sum_{g=1}^{ni}\sum_{v=1}^{ni}\Big{(}\lambda_{g}E_{y_{g}^{a},g}+\mu_{v} \widetilde{T}_{y_{v-1}^{a}y_{v}^{a}}\Big{)}\right) \tag{7}\]  
\(Z\Big{(}\hat{h}_{ni}^{L}\Big{)}\) is the normalization factor, \(E_{y_{g}^{a},g}\) is the probability of label \(y_{g}^{a}\) corresponds to \(\hat{h}_{g}^{L}\), \(\widetilde{T}_{y_{v-1}^{a}y_{v}^{a}}\) is the probability of label \(y_{v-1}^{a}\) corresponds to \(\hat{h}_{v-1}^{L}\) under the premise of label \(y_{v}^{a}\) corresponds to \(\hat{h}_{v}^{L}\), \(\lambda_{g}\) and \(\mu_{v}\) are hyperparameters. Therefore, the CRF is trained by calculating the maximum log-likelihood function:  
\[\text{max}:\mathcal{L}_{A}=\sum_{(S,y^{a})\in R}logP(y^{a}|\hat{h}^{L}) \tag{8}\]  
To sum up, we train the event extraction model LLSD in Chinese DTE by minimizing the following loss function:  
\[\mathcal{L}(\Theta)=-(\lambda_{1}\mathcal{L}_{T}+\lambda_{2}\mathcal{L}_{A}) \tag{9}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are weights, \(\Theta\) is the sum of the parameters of the model LLSD. (2) For long sentence-level EAs, being recognized by the pattern matching rules that are given in Table 7 is not only resulted from the low detection accuracy of machine learning methods as the EAs contain a generous number of words, but also because the sentence structure of examination and test event corresponding to long sentence-level EA is relatively regular and has distinct syntactic characteristics.