## 4 Experiments
### Results
For the Turkish dataset, the overall accuracy increases slightly from 91.47% to 92.44%, with a gain in the OOV accuracy from 87.97 to 88.45%. The ambiguous case accuracy improves by nearly 2%. In the kD1 dataset, the overall accuracy improves by 6%, with the OOV accuracy reaching 85.27% and the ambiguous case accuracy increasing by over 9%. The kD2 dataset shows an over 6% improvement in overall accuracy, with similar increases in the OOV and ambiguous case accuracy. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Dataset** & **Distance** & **Overall Acc.** & **OOV Acc.** & **Ambig. Acc.** \\ \hline \multirow{6}{*}{kD1} & cosine similarity & 89.67 & 82.18 & 84.20 \\ \cline{2-5}  & Euclidean & 92.14 & 85.98 & 87.98 \\ \cline{2-5}  & dot product & 92.39 & 86.46 & 88.36 \\ \cline{2-5}  & linear layer & 73.47 & 72.68 & 59.41 \\ \hline \multirow{6}{*}{kD2} & cosine similarity & 87.06 & 84.34 & 79.99 \\ \cline{2-5}  & Euclidean & 88.46 & 86.24 & 82.15 \\ \cline{2-5}  & dot product & 87.83 & 85.71 & 81.18 \\ \cline{2-5}  & linear layer & 80.97 & 80.88 & 70.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy results of MD for Kazakh datasets for overall, OOV, and ambiguous cases. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Distance Metrics** & **Overall** & **OOV** & **Ambig.** \\ \hline \multirow{6}{*}{kD1} & \multicolumn{3}{c}{root} \\ \cline{2-5}  & cosine similarity & 97.71 & 94.77 & 96.50 \\ \cline{2-5}  & Euclidean & 98.33 & 96.44 & 97.45 \\ \cline{2-5}  & dot product & 98.27 & 95.96 & 97.35 \\ \cline{2-5}  & linear layer & 93.75 & 93.59 & 90.44 \\ \cline{2-5}  & \multicolumn{3}{c}{POS} \\ \cline{2-5}  & cosine similarity & 96.04 & 94.06 & 93.95 \\ \cline{2-5}  & Euclidean & 96.60 & 95.01 & 94.79 \\ \cline{2-5}  & dot product & 96.35 & 94.29 & 94.42 \\ \cline{2-5}  & linear layer & 81.08 & 85.51 & 71.05 \\ \cline{2-5}  & \multicolumn{3}{c}{morpheme} \\ \cline{2-5}  & cosine similarity & 91.22 & 84.09 & 86.57 \\ \cline{2-5}  & Euclidean & 93.69 & 87.89 & 90.35 \\ \cline{2-5}  & dot product & 93.94 & 88.36 & 90.73 \\ \cline{2-5}  & linear layer & 80.27 & 80.04 & 69.82 \\ \hline \hline \multirow{6}{*}{kD2} & \multicolumn{3}{c}{root} \\ \cline{2-5}  & cosine similarity & 98.16 & 97.89 & 97.16 \\ \cline{2-5}  & Euclidean & 98.65 & 98.21 & 97.91 \\ \cline{2-5}  & dot product & 98.17 & 97.58 & 97.09 \\ \cline{2-5}  & linear layer & 96.42 & 96.32 & 94.47 \\ \cline{2-5}  & POS & & \\ \cline{2-5}  & cosine similarity & 92.85 & 92.96 & 88.95 \\ \cline{2-5}  & Euclidean & 94.40 & 95.06 & 91.34 \\ \cline{2-5}  & dot product & 93.81 & 94.32 & 90.44 \\ \cline{2-5}  & linear layer & 89.52 & 91.81 & 83.79 \\ \cline{2-5}  & \multicolumn{3}{c}{morpheme} \\ \cline{2-5}  & cosine similarity & 87.64 & 84.77 & 80.88 \\ \cline{2-5}  & Euclidean & 88.85 & 86.45 & 82.75 \\ \hline dot product & 88.27 & 90.35 & 81.85 \\ \cline{2-5}  & linear layer & 81.46 & 81.59 & 71.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy results of MD for Kazakh datasets for root, POS, and morpheme. Table 5 provides a detailed breakdown of the accuracy improvements across the root, POS, and morpheme categories. After using LLM, the accuracy for the ambiguous case improves across all categories. For the Turkish dataset, the accuracy for the root increases slightly, with the POS and morpheme categories seeing gains of around 0.9% and 2%, respectively. In the Kazakh datasets, the model cMD\({}_{plm}\) shows significant improvements. For kD1, there is a nearly 1.5% increase in the root ambiguous accuracy, an over 5% gain in POS, and a nearly 8% improvement in morpheme ambiguous accuracy compared with cMD\({}_{mlp}\). In the kD2 dataset, the model shows similar results, with improvements of 1.5% in root, 6% in POS, and 10% in morpheme ambiguous accuracy.