# LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
## 4 Experiments
### Discussion
Compared with the two-stage ETW detection method, the R and F1 value of experiment with document consistency features are improved from 79.03% to 80.61% and from 82.23% to 83.57%, respectively, which indicate that document consistency features can supplement the information difficult to extract on the long sentence-level and improve the performance of the model. The trend of F1 value change of different events under different feature combinations are shown in Figure 5, in which the entity type feature T of intra sentence significantly improves the ETW detection effect on chemotherapy, treatment, operation, and examination events, and the number feature N of different entities of inter sentences significantly improves the ETW detection effect on the pathological test, immunohistochemistry, operation, and examination events. The two-stage experiment had a great influence on treatment, operation, examination, and chemotherapy events, while the document consistency features S improved the results of ETW detection of all event types. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Event Types**} & \multicolumn{3}{c}{**Chemotherapy**} & \multicolumn{3}{c}{**Examination**} \\ \cline{3-7}  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{Two-stage} & Baseline + T & 95.06 & 88.16 & **91.48** & 88.61 & 69.31 & 77.78 \\  & Baseline + T + N & 95.12 & 88.32 & 91.59 & 89.87 & 70.29 & 78.88 \\ \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Event Types**} & \multicolumn{3}{c}{**Operation**} & \multicolumn{3}{c}{**General treatment**} \\ \cline{3-7}  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{Two-stage} & Baseline + T & 77.87 & 85.91 & 81.69 & 87.87 & 83.53 & 85.65 \\  & Baseline + T + N & 80.75 & 87.97 & 84.21 & 87.86 & 84.31 & 85.91 \\ \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Event Types**} & \multicolumn{3}{c}{**Admission**} & \multicolumn{3}{c}{**Immunohistochemistry**} \\ \cline{3-7}  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{Two-stage} & Baseline + T & 84.33 & 75.66 & 79.76 & 76.05 & 83.65 & 79.67 \\  & Baseline + T + N & 85.20 & 76.62 & 80.68 & 76.12 & 81.87 & 78.89 \\ \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Event Types**} & \multicolumn{3}{c}{**Pathological Test**} & \multicolumn{3}{c}{**Diagnosis**} \\ \cline{3-7}  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{Two-stage} & Baseline + T & 90.71 & 75.67 & 82.51 & 91.64 & 80.22 & 85.55 \\  & Baseline + T + N & 91.45 & 76.91 & 83.55 & 91.56 & 79.06 & 84.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of trigger detection by two-stage method. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Event Types**} & \multicolumn{3}{c}{**ALL-TOTAL**} \\ \cline{3-4}  & & **P** & **R** & **F1** \\ \hline B 1 + P1 2 + R1 3 & 86.31 & 79.42 & 82.72 \\ B + P1 + R1 + T + PT 4 + RT 5 & 86.54 & 80.13 & 83.21 \\ B + P1 + R1 + T + N + PTN 6 + RTN 7 & 85.84 & 79.36 & 82.47 \\ B + P1 + R1 + N + PN 8 + RN 9 & **86.97** & **80.61** & **83.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of trigger detection based on long-level semantic dependency feature. In terms of EA extract, the influence of different word vector pre-training models of Word2 Vec, ELMO, and BERT on the recognition of ETW and EA are compared in this paper; as shown in Table 13, the effect of BERT is better than that of Word2 Vec and ELMO, which is because Word2 Vec does not consider the influence of context information on the word vector when constructing the text vector so that the word vector generated by the same word is invariant. ELMO employs stacked BiLSTM to make the generated word vectors contain more context information of the vocabulary, so the ELMO is better than Word2 Vec.