# Normalization of Web of Science Institution Names Based on Deep Learning
## 4 Experiments
### Results
#### 4.4.3 Hyperparameters  
To investigate the impact of different hyperparameters on the experimental results, we trained the proposed model using the following parameter configurations, as shown in Table 6. We varied the number of randomly sampled contexts per entity from 1 to 20 and the maximum context length from 5 to 20. For Char-CNN, we changed the number and size of the convolutional filters. The margin value (m) in the loss function was varied from 0 to 0.8. We also experimented with different optimizers during training.  
Figure 4 depicts the overall trend of the F1 score increasing as the number of contexts increases, indicating that the model generally performs better with more context information. This aligns with expectations, as having more context information allows for better differentiation between two institution names as the same or different entities. However, it can be observed from the graph that all metrics decrease when the number of contexts is five. Upon analyzing the data, this is mainly attributed to the imbalance in the number of contexts for institution names. Some institutions have insufficient contexts to meet the specified number, causing the model to overly rely on features from institution names with an adequate number of contexts and neglect those with fewer contexts. When the maximum context length is set to 15, the model achieves the best F1 score. This is because longer contexts may introduce noise, while shorter contexts may provide less information.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **VALUE** \\ \hline epoch & 10 \\ lstm\_size & 512 \\ word\_size & 200 \\ context\_number & \(20,15,10,5,3,1\) \\ context length & \(20,15,10,5\) \\ filter\_size & \(1,2,3,4,5,6\) \\ filter\_amount & \(100,200,300\) \\ m (margin) & \(0,0.2,0.4,0.6,0.8\) \\ optimizer & _Adam_, _RMSProp_, _Adadelta_, _Adagrad_ \\ loss function & Siamese loss \\ batch size & 2 \\ learning rate & \(2\times 10^{-5},1\times 10^{-5},1\times 10^{-4},5\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiment result.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Type** & **Reason** & **Improvement Direction** \\ \hline \multirow{6}{*}{Different institutions that are similar both literally and semantically.} & The two institutions Universit & \\  & Ben Turin and Universtil Ben & \\  & Turku have similar names and & Introduce better features, such \\  & Turku have a large number of the & as author name features and \\  & same sub-institutions, such as & geographic attribute features. \\  & Compters, Czes, Depatment, & \\  & and Deputklinxczens. & \\ \hline \multirow{6}{*}{Ambiguous institution aliases in authority files.} & The alias of “Perbright & \\  & Institute” is “Institute Animal & \\  & Health”, which is the same as & \\  & the aliases of several & \\  & secondary institutions, & top to bottom to avoid \\  & resulting in a mismatch of & identical or similar aliases. \\ \hline \multirow{2}{*}{The choice of institutional context is not reasonable.} & Contextual selection methods & \\  & are not perfect enough, which & \\  & affects model matching. & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error analysis results.  
As the margin value (m) increases, the F1 score, precision, and recall all show a decreasing trend. This indicates that learning from negative examples is more important for institution synonym recognition compared to positive examples.