Ta'limda raqamli texnologiyalarni tadbiq etishning zamonaviy tendensiyalari va rivojlanish omillari
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INTEGRATING MORPHOLOGICAL ANALYSIS INTO MACHINE
LEARNING MODELS FOR LANGUAGE PROCESSING
Sultanbayeva Oltinoy Omonbay kizi
Independent researcher
Annotation:
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
Introduction
This study investigates how incorporating morphological information into
machine learning models can enhance performance in language processing tasks, with
a focus on lemmatization. Morphology, as a core component of linguistic theory,
provides valuable structural information that many statistical models tend to overlook.
By combining linguistic insights with computational techniques, the research aims to
bridge the gap between theory and practice in NLP.
Literature analysis and methodology
The theoretical foundations of morphology, as presented by Aronoff (1976) and
Booij (2005), highlight the structural role of affixation, root identification, and
inflection in language. Cotterell and Heigold (2017) showed that morphological
tagging benefits from character-level modeling across languages. Vania and Lopez
(2017) explored how different input representations capture morphology in neural
models. Jurafsky and Martin (2023) emphasize that subword modeling strategies like
byte-pair encoding (Sennrich et al., 2016) are particularly helpful in handling
morphological variation, especially in low-resource settings. Overall, the literature
supports the claim that morphological awareness contributes to model robustness and
generalization. This study used a comparative design involving two lemmatization
models: a baseline (without morphology) and a morphology-aware version. Both were
trained on parallel datasets in Arabic, Russian, and Finnish using annotated corpora.
Results
Across all languages tested, the morphology-aware model outperformed the
baseline in lemmatization accuracy. In Arabic, it effectively handled clitics and
Ta'limda raqamli texnologiyalarni tadbiq etishning zamonaviy tendensiyalari va rivojlanish omillari
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irregular verbs. In Russian, it showed stronger disambiguation across case-marked
forms. In Finnish, it correctly processed long, agglutinative word forms. The model
was more accurate, especially with rare or unseen word types, and better at generalizing
in morphologically complex contexts.
Discussion
The findings affirm that morphological features significantly enhance machine
learning performance in lemmatization. Integrating linguistic knowledge allows for
better word structure modeling and improved results across different languages. This
validates the relevance of linguistic theory in computational practice. However,
limitations include reliance on annotated data and increased model complexity. Still,
the benefits of linguistic integration outweigh these challenges, especially for under-
resourced languages.
Conclusion
This thesis demonstrates that integrating morphological analysis into NLP models
significantly improves lemmatization, especially for morphologically rich languages.
It supports a hybrid approach that combines linguistic insight with computational
methods, achieving more interpretable and accurate models. Future research may
extend this framework to other NLP tasks and explore unsupervised morphology
learning in low-resource settings.
References:
Aronoff, M. (1976). *Word formation in generative grammar*. MIT Press.
Booij, G. (2005). *The grammar of words: An introduction to linguistic
morphology*. Oxford University Press.
Cotterell, R., & Heigold, G. (2017). Cross-lingual character-level neural
morphological tagging. In *Proceedings of EMNLP* (pp. 759–769).
https://doi.org/10.18653/v1/D17-1079
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of
deep bidirectional transformers for language understanding. *arXiv preprint
arXiv:1810.04805*. https://arxiv.org/abs/1810.04805
Jurafsky, D., & Martin, J. H. (2023). *Speech and language processing* (3rd ed.,
draft). https://web.stanford.edu/~jurafsky/slp3/
Sennrich, R., Haddow, B., & Birch, A. (2016). Neural machine translation of rare
words with subword units. In *Proceedings of ACL* (pp. 1715–1725).
https://aclanthology.org/P16-1162
Vania, C., & Lopez, A. (2017). From characters to words to in between: Do we
capture morphology? In *Proceedings of EACL* (pp. 751–761).
https://aclanthology.org/E17-1071
