## 4 Experiments
### Results  
In the experimental results, the accuracy for three types of tokens is reported as follows: (i) all tokens; (ii) ambiguous tokens (those with at least two analyses); (iii) OOV tokens. Table 2 shows the overall, OOV, and ambiguous case accuracy for four distance measures: cosine similarity, Euclidean distance, dot product, and linear layer. For the kD1 dataset, the dot product distance metric achieved the highest overall accuracy, with 92.39% accuracy, 86.46% for OOV cases, and 88.36% for ambiguous cases. The Euclidean distance metric also performed strongly, with 92.14% overall accuracy. The cosine similarity achieved 89.67% overall accuracy, while the linear layer produced the lowest results, with an overall accuracy of 73.47%. In the kD2 dataset, the Euclidean distance yielded the highest overall accuracy (88.46%), followed by dot product at 87.83%. The cosine similarity showed an overall accuracy of 87.06%, and the linear layer again demonstrated the lowest performance, with 80.97% accuracy. Table 3 reports the accuracy results for MD on the Kazakh datasets, measured across three aspects: root, POS, and morpheme. For the kD1 dataset, the Euclidean distance yielded the highest accuracy for root disambiguation, with 98.33% overall accuracy, followed by the dot product at 98.27%. In the OOV and ambiguous cases, the Euclidean distance achieved 96.44% and 97.45%, respectively. The cosine similarity yielded 97.71% overall accuracy. The linear layer achieved 93.75% overall accuracy, with a drop to 90.44% for ambiguous cases. In POS disambiguation, the Euclidean distance reached 96.60% overall accuracy, while the dot product scored 96.35%. The linear layer performed at 81.08%, with a significant decrease to 71.05% for ambiguous cases. For morpheme disambiguation, the dot product reached 93.94% overall accuracy and 88.36% OOV accuracy, with the Euclidean distance at 93.69% overall. The cosine similarity reached 91.22% overall accuracy, and the linear layer reached 80.27%, with the lowest ambiguous case accuracy of 69.82%. In the kD2 dataset, the Euclidean distance achieved 98.65% overall accuracy for root disambiguation, followed by the dot product at 98.17% and the cosine similarity at 98.16%. The linear layer achieved 96.42%. For POS disambiguation, the Euclidean distance reached 94.40%, while the dot product followed with 93.81%. The linear layer achieved 89.52%. For morpheme disambiguation, the Euclidean distance reached 88.85% overall accuracy, with the dot product at 88.27%. The cosine similarity achieved 87.64%, and the linear layer reached 81.46%. Across both datasets, the Euclidean distance and the dot product consistently produced higher accuracy in all categories. The linear layer consistently showed lower results, particularly in ambiguous cases. The possible reason behind why the linear layer showed lower results compared to the other metrics is the large tagset in morphological disambiguation (MD). The linear layer uses a parameter \(\mathbf{W}_{t}\in\mathbb{R}^{d\times 1}\), which computes the concatenation of the context and the representations of different analyses. Here, \(d\) represents the dimension of the concatenation of the context and one analysis embedding. Since the number of unique analyses is large and \(W_{t}\) is a vector parameter, it becomes difficult to effectively capture the relationship between the context and the numerous different analyses. In comparison, in named entity recognition (NER), there is a fixed tagset, and in that case, the parameter's dimension is fixed to a specific label, making it easier to model the relationships. Table 4 shows that fine-tuning with PLMs (cMD\({}_{plm}\)) results in improved accuracy across both languages compared to the baseline (cMD\({}_{mlp}\)) that uses a multilayer perceptron. Both of the models cMD\({}_{plm}\) and cMD\({}_{mlp}\) were trained and tested without using any external designed features.