WORD REPRESENTATION IN VECTOR SPACE USING WORD2VEC MODEL
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Abstract:
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.
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