The Role of Morphology in Natural Language Processing: A Comparative Study of Agglutinative and Fusional Languages
DOI:
https://doi.org/10.24113/ijohmn.v10i5.359Keywords:
morphology, NLP, agglutinative languages, fusional languages, Turkish, Spanish, machine translationAbstract
This study investigates how morphological typology affects the performance of Natural Language Processing (NLP) systems. By comparing Turkish (agglutinative) and Spanish (fusional), we analyze tokenization, part-of-speech tagging, dependency parsing, and machine translation accuracy. The findings reveal that morphological richness introduces challenges for NLP algorithms, particularly in agglutinative languages, and underscore the need for typology-aware model design. This research contributes to computational linguistics by highlighting the importance of morphological structure in language technology development.
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