# LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
## 4 Experiments
### Discussion  
In terms of ETW detection, the low accuracy of ETW detection is directly reduced by the ETWs that infrequently appear in the candidate ETW dictionary that exists in HPI texts. The accuracy of ETW module is improved by setting a two-stage experiment; the first stage determines whether each candidate ETW, verb, and gerund are ETW through direct binary classification by using features of word, part of speech, and the number of different entities type. The second stage determines the type by multi-classification for the ETWs selected in the first stage. As shown in Table 11, the F1 value of the two-stage ETW detection has been significantly improved under the same feature combination. For example, compared with the result in Table 10, the F1 value of the chemotherapy event increased from 89.91% to 91.48% through the two-stage method under the feature combination of \"Baseline + T\". \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods**Event Types & \multicolumn{3}{c}{**EA Recognition**} & \multicolumn{3}{c}{**EA Classification**} \\ \cline{2-7} \cline{6-7} \multicolumn{1}{c}{} & **P** & **R** & **P** & **R** & **P** & **R** \\ \hline Word-based C-BiLSTM & 59.8 & 52.7 & 59.8 & 52.7 & 59.8 & 52.7 \\ Char-based C-BiLSTM & 61.2 & 58.5 & 61.2 & 58.5 & 61.2 & 58.5 \\ LSLSD (our) & **66.3** & **67.5** & **66.3** & **67.5** & **66.3** & **67.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of event argument extraction under different methods. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods**Event Types & \multicolumn{3}{c}{**Chemotherapy**} & \multicolumn{3}{c}{**Examination**} \\ \cline{2-7} \multicolumn{1}{c}{} & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Baseline & 94.71 & 82.06 & 87.93 & 87.14 & 60.39 & 71.34 \\ Baseline + T & 95.29 & 85.11 & **89.91** & 87.45 & 63.23 & **73.39** \\ Baseline + T + N & 95.31 & 86.06 & 90.44 & 89.47 & 67.32 & **76.83** \\ \hline \hline \multirow{2}{*}{**Methods**} & **Operation** & \multicolumn{3}{c}{**General Treatment**} \\ \cline{2-7} \multicolumn{1}{c}{} & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Baseline & **68.96** & 86.25 & **76.64** & 86.98 & 76.95 & 81.66 \\ Baseline + T & **74.26** & 87.28 & **80.25** & 87.22 & 81.48 & 84.25 \\ Baseline + T + N & 75.89 & 87.62 & 81.33 & 87.24 & 81.57 & 84.31 \\ \hline \hline \multirow{2}{*}{**Methods**} & **Acmission** & \multicolumn{3}{c}{**Immunohistochemistry**} \\ \cline{2-7} \multicolumn{1}{c}{} & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Baseline & 78.34 & 73.44 & 75.81 & 69.91 & 76.51 & 73.06 \\ Baseline + T & 81.22 & 75.47 & 78.24 & 70.27 & 81.29 & 75.38 \\ Baseline + T + N & 82.16 & 76.50 & 79.23 & 75.46 & 82.60 & 78.87 \\ \hline \hline \multirow{2}{*}{**Methods**} & **Event Types** & \multicolumn{3}{c}{**Pathological Test**} & \multicolumn{3}{c}{**Diagnosis**} \\ \cline{2-7} \multicolumn{1}{c}{} & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Baseline & 87.84 & 77.51 & 82.35 & 86.91 & 81.77 & 84.26 \\ Baseline + T & 88.13 & 77.28 & 82.35 & 89.09 & 80.90 & 84.80 \\ Baseline + T + N & 91.06 & 76.81 & 83.33 & 91.26 & 80.05 & 85.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of trigger detection under different feature combinations. In addition, the event sentence with the accuracy of TWE classification is less than 40% by fusing extended features of short-level semantic dependence named untrusted event sentence; otherwise, it is a trusted event sentence. The untrusted event sentences are classified a second time by adding long-level semantic dependence features \(f_{7}\) and other extended features of enter sentences to improve the accuracy of ETW detection. From the ETW detection results under different features combinations in Table 12, the result of the \"P1 + R1 + B + N + PN + RN\" combination is the best.