Eurasian Journal of Mathematical Theory and Computer Sciences 5-jild 5-son (2025) · 54–59-betlar

WORD REPRESENTATION IN VECTOR SPACE USING WORD2VEC MODEL

Рахманов, Аскар, Исхакова, Наргиза, Абдувалиева, Зебинисо

DOI: 10.5281/zenodo.15524744 · Manbada o'qish →

Annotatsiya

The article discusses the word2vec model, which is an effective method for learning vector representations of words, widely used in natural language processing tasks. The main architectures of the model - Skip-gram and CBOW, as well as key parameters that affect the quality of the resulting vector representation are described. It is shown that the use of word2vec allows transforming words into dense vectors that reflect their semantic and syntactic properties, which significantly improves the results compared to traditional text representation methods.

Word2Vec, Skip-gram and CBOW, vector representation of data, natural language processing, text data classification.Word2Vec, Skip-gram и CBOW, векторное представление данных, обработка естественного языка, классификация текстовых данных.

Metadata manbasi: jurnal OAI-PMH arxivi · Sindex to'liq matnni saqlamaydi, manbaga havola beradi.