# Word Vector Representation of Latin Cuengh Based on Root Feature Enhancement
## 4 Experiment and Analysis
### Internal Evaluation Experiment  
The internal evaluation is based on the training effect of the model itself, including the word similarity clustering test and word analogy test. To obtain the effect of the model training word vector, several word vector training methods are selected for comparison, including CBOW, skip-gram, Glove, BERT, and the BERT-based root model RootBERT. In the experiment, this paper assesses the accuracy of different models for evaluating the similarity of words. Specifically, for a given word, the model gives the top-ranked words with similar semantics and calculates the model's accuracy according to the words with the given similarity. The results are shown in Table 2.  
Table 2 shows the evaluation results of different models for the similarity index. The paper evaluates the top 5 and top 10 words. It can be seen from the table that the accuracy of selecting the top 5 most similar words is higher than the accuracy rate of the top 10, and the accuracy of the model will decrease as it selects more similar words. However, our root-based BERT word embedding method achieves the best results. We believe that the root pattern training method mainly relies on the structural characteristics of Latin Cuengh word formation. Through effective decomposition, semantic information of words can be greatly obtained, which is expressed and remembered by the model during the training process, thereby demonstrating good performance on downstream semantic tasks. To further verify the semantic accuracy of the model word vector representation, the word \"Mwngzygang (China)\" was selected for the word vector similarity test, and the top 10 words were selected. In the experiment, the similarity value ranged from 0 to 5. The larger the score is, the closer the semantics of the two words are. The experimental results are shown in Table 3. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Categories** & **Number of Texts** & **Each Text Contains About the Number of Cuengh Words** & **Number of Similar or Related Word Pairs** & **Number of Proportion Pairs** \\ \hline Cultural and customs & 1674 & 1800 & 383 & 44.23\% \\ \hline Politics and economy & 1230 & 1780 & 258 & 29.79\% \\ \hline Characteristics and specialties & 972 & 1850 & 225 & 25.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter attributes of several different categories of data. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Accuracy of Top 5** & **Accuracy of Top 10** \\ \hline CBOW & 83.35\% & 78.66\% \\ \hline Skip-Gram & 84.78\% & 79.03\% \\ \hline Glove & 85.63\% & 80.34\% \\ \hline BERT & 87.38\% & 83.54\% \\ \hline RootBERT (Ours) & 89.21\% & 85.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Similarity evaluation results of different models. Table 3 shows the evaluation results of the top 10 most similar words for the word \"Mwngzygang\". Obviously, the words \"Mwngzygang (China)\" and \"Cunghyraz Yitzminz Gunghozgoz (People's Republic of China)\" both represent China, so the semantics are the closest, which is determined by the data in the corpus. The information obtained directly, and the words ranked 2 to 6 are Meigoz (America), Fazgoz (France), Dwzgoz (Germany), Yinghgoz (England), and Hanzgoz (Korea). These words are also very characteristic. Obviously, in the Chinese expression, these words all contain the word \"country\", and similarly, their Latin Cuengh expressions also contain the root \"goz\", so the similarity scores of these words are very high. Close to the word \"China\", the seventh rank is \"Ezlozswh (Russia)\"; although Latin Cuengh does not contain the root \"goz\", its score is still high. We find that the word \"Ezlozswh (Russia)\" in the corpus should often appear with the words \"Mwngzygang (China)\" and \"Meigoz (America)\". The words ranked 8 th to 10 th are Gyahnazda (Canada), Sinhgyahboh (Singapore), and Nditbonj (Japan).