SOʻZ SHAKLLARINING ASOSINI ANIQLASH ALGORITMI
Keywords:
NLP, Lemmatization, Morphological analysis, word form, spacy, stemming, modelAbstract
Identifying the root form of words (stemming) plays a crucial role in natural language processing (NLP) as it forms the foundation of morphological analysis. By determining word forms, grammatical and semantic features are identified, which helps in understanding the language and developing relevant technologies. This process is essential in NLP applications such as text classification, translation, and communication systems. Various methods are available to identify word forms, including rule-based approaches, morphological modules, lemmatization, stemming, machine learning, and deep learning techniques. Each approach has its own advantages and drawbacks. For example, rule-based methods are simple and effective but may not fully capture complex grammatical structures. Morphological modules provide high accuracy but require large datasets. Lemmatization and stemming methods help account for subtle grammatical features in the language, though they are not always perfect. Deep learning and machine learning methods allow for more accurate analysis of language morphology but require significant computational
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