Solving anaphora in the kazakh language based on machine learning

In natural language processing (NLP), resolving pronominal anaphora is a critical task that enhances the coherence and understanding of text. This paper presents a novel algorithm for solving pronominal anaphora in the Kazakh language, a language with rich morphological features and free word order that poses unique challenges for NLP. Our approach involves a combination of rule-based and machine learning techniques. We begin by developing a set of linguistic rules that capture the syntactic and semantic constraints specific to Kazakh. These rules help identify potential antecedents for pronominal references. Next, we incorporate a machine learning model trained on annotated Kazakh corpora to refine the selection process and improve accuracy. The algorithm was evaluated on a benchmark dataset of Kazakh texts, demonstrating significant improvements over baseline methods. Our results show that the hybrid approach effectively captures the nuances of Kazakh pronominal anaphora, achieving an accuracy of 79% in correctly resolving anaphoric references. This work represents a significant step forward in Kazakh NLP, providing a robust tool for text analysis and machine translation applications. Future work will focus on expanding the dataset and refining the model to handle more complex linguistic structures.