INTEGRATING MORPHOLOGICAL ANALYSIS INTO MACHINE LEARNING MODELS FOR LANGUAGE PROCESSING

Аннотация

This thesis explores the integration of linguistic morphological analysis into machine learning models for natural language processing (NLP). It focuses on how the inclusion of explicit morphological features, such as roots, affixes, and grammatical tags, can improve tasks like lemmatization. The study targets morphologically rich languages and uses both theoretical frameworks and experimental evaluation to support the findings. Keywords: Computational linguistics, morphological analysis, machine learning, lemmatization, natural language processing, low-resource languages