## 5 Discussion  
The error distribution for the kD1 dataset shows several key misclassification patterns in Figure 2. The most frequent error (over 5.5%) is the misclassification of singular accusative forms (n px3 sg acc) as plural accusative forms (n px3 pl acc). Another common error is the prediction of adjectival forms instead of plural noun forms (adj px3 pl acc \(\rightarrow\) n px3 pl acc), contributing to nearly 4% of the errors. Additionally, there are misclassifications in the locative and ablative cases, such as (n px3 pl loc \(\rightarrow\) n px3 sg loc), reflecting the model's difficulty in distinguishing singular and plural forms across these cases.  
Verb-related errors, especially between present and past forms (v-iv presct \(\rightarrow\) v-iv pastvadv), also appear in the error distribution. The model struggles to differentiate between singular and plural noun forms, shows challenges with noun-adjective agreement in number and case, and has difficulty distinguishing between tense and auxiliary verb forms.  
Figure 3 shows the error distribution for the kD2 dataset. The most frequent error, making up over 5% of the misclassifications, involves confusing masculine anthroponym noun phrases (np ant m nom) with simple nouns (n nom). Another error above 4% occurs between perfect participles (v tv prc_perf) and verbal adverbs (v tv gna_perf). The third most common error, around 3%, involves the misclassification of topological nouns (np top nom) as attributive nouns (np top attr).  
Figure 4 presents the error distribution for the Turkish dataset and shows several cases where the model has difficulty. The most common error, accounting for almost 10% of cases, is the misclassification of adjectives as singular nouns in the nominative case (adj \(\rightarrow\) noun a3 sg pnon nom), indicating difficulty in separating these two grammatical functions. About 8% of errors involve confusing proper nouns with common singular nouns (noun prop a3 sg pnon nom \(\rightarrow\) noun a3 sg pnon nom), suggesting challenges in distinguishing proper from regular nouns. Approximately 6% of the errors occur when singular accusative nouns are misclassified as singular nominative nouns with possession (noun a3 sg pnon acc \(\rightarrow\) noun a3 sg p3 sg nom). In addition, the model frequently confuses verbs in the past tense with narrative mood and singular past-tense verbs (verb pos narr adj zero \(\rightarrow\) verb pos narr a3 sg), reflecting difficulty with tense, mood, and number. Lastly, about 2.5% of errors are due to misclassifying adverbs as adjectives (adverb \(\rightarrow\) adj).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Model** & **Overall** & **OOV** \\ \hline \multirow{4}{*}{kD1} & HMM (decoding) [26] & 84.91 & 71.73 \\ \cline{2-4}  & Voted-Perceptron (fea.+decoding) [26] & 90.53 & 82.42 \\ \cline{2-4}  & cMD\({}_{plm}\) & 92.64 & 85.27 \\ \hline \multirow{2}{*}{Turkish} & Voted-Perceptron (fea.+decoding) & 91.89 & 87.98 \\ \cline{2-4}  & cMD\({}_{plm}\) & 92.44 & 88.45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with previous work; fea. stands for a set of features, and decoding indicates that the method uses Viterbi decoding to find the best path among the analyses.  
Figure 3: Error distributions of analysis (roots are excluded) for kD2 dataset.  
Figure 2: Error distributions of analysis (roots are excluded) for kD1 dataset.