## 6 Conclusions  
Morphological disambiguation is a long-standing challenge in processing MCLs. It is akin to POS tagging, but with a key distinction: in MD, it is not just the POS tag that needs to be correctly predicted, but also the lemma or root form of the word along with its corresponding morphological tags. This adds layers of complexity to the task compared to traditional POS tagging. In this paper, a contrastive learning approach for morphological disambiguation (MD) using pre-trained language models (PLMs) was presented. The intention behind the contrastive loss of the proposed approach is to reduce the distance between the correct analysis and contextual embeddings while maintaining a margin between incorrect with contextual embeddings. One of the aims of the paper is to explore the impact of fine-tuning a PLM for MD on Kazakh, a low-resource language as well as Turkish. Traditional approaches to MD, like HMM-based and feature-engineered models, will not be able to capture long-term dependencies. In this regard, the study leveraged the contextualized embeddings from the PLMs, which allowed the model to enhance its handling of ambiguous cases and OOV tokens without depending on other features. The experimental results show that the fine-tuned PLM gave better MD results without using any designed features, especially for Kazakh. Further, the disambiguation analysis performed with different distance measurements shows that the Euclidean distance and dot product yield the best distance between context and morphological analysis embeddings. These results show that PLMs are useful in enhancing the performance of MD tasks for various morphologically complex languages, including low-resource ones like Kazakh. The error analysis revealed that the model faces challenges in differentiating singular from plural noun forms (it has strong long-term dependency), struggles with maintaining noun-adjective agreement in both number and case, and encounters difficulties in distinguishing between tense and auxiliary verb forms in the case of Kazakh. The results for the Turkish dataset revealed that the model struggled to distinguish between adjectives and nouns, properly identifying proper nouns versus common nouns, handling case and number distinctions (especially with possession and plurality), and managing complex verb forms involving tense and mood. Figure 4: Error distributions of analysis (roots are excluded) for Turkish dataset. Overall, cMD\({}_{plm}\), which uses a contrastive learning method and is incorporated with LLM, achieves an overall accuracy of 92.64% for Kazakh (kD1). It outperforms the HMM-based approach by approximately 7% and the voted-perceptron approach (a set of features combined with decoding techniques) by 2%. For Turkish, it slightly outperforms the voted-perceptron approach which uses a group of features with decoding. Future work can be summarized as follows: (i) to improve the model seeking to solve the existing errors; (ii) to explore how morphological analysis and disambiguation can be integrated into a unified learning framework through large language models, thus significantly improving the performance of the MD models in various linguistic contexts; (iii) to explore the question of how to effectively use the morphological features, since long-term dependency exits between these tags. Conceptualization, A.T. and G.T. ; methodology, G.T. and A.T.; software, G.T. and A.T.; validation, A.T. and G.T. ; formal analysis, A.T. and G.T. ; investigation, G.T. and A.T.; resources, A.T. and G.T. ; data curation, G.T. and A.T.; writing--original draft preparation, A.T. and G.T. ; writing--review and editing, A.T. and G.T. ; visualization, A.T. and G.T. ; supervision, A.T. and G.T. ; project administration, R.M. ; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript. Not applicable.