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High-Dimensional Vector Semantics

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 نشر من قبل Mircea Andrecut Dr
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English
 تأليف M. Andrecut




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In this paper we explore the vector semantics problem from the perspective of almost orthogonal property of high-dimensional random vectors. We show that this intriguing property can be used to memorize random vectors by simply adding them, and we provide an efficient probabilistic solution to the set membership problem. Also, we discuss several applications to word context vector embeddings, document sentences similarity, and spam filtering.

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