CONVERGENCE OF THE PARTITION FUNCTION IN THE STATIC WORD EMBEDDING MODEL

K. Mynbaev; Zh Assylbekov

doi:10.32523/2077-9879-2022-13-4-70-81

CONVERGENCE OF THE PARTITION FUNCTION IN THE STATIC WORD EMBEDDING MODEL

K. Mynbaev
, Zh Assylbekov

Kazakh-British Technical University

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

We develop an asymptotic theory for the partition function of the word embeddings model word2vec. The proof involves a study of properties of matrices, their determinants and distributions of random normal vectors when their dimension tends to infinity. The conditions imposed are mild enough to cover practically important situations. The implication is that for any word i from a vocabulary W, the context vector c_i is a reflection of the word vector w_i in approximately half of the dimensions. This allows us to halve the number of trainable parameters in static word embedding models.

Original language	English
Pages (from-to)	70-81
Number of pages	12
Journal	Eurasian Mathematical Journal
Volume	13
Issue number	4
DOIs	https://doi.org/10.32523/2077-9879-2022-13-4-70-81
Publication status	Published - 2022

Funding

Supported by the ISAAC (International Society for Analysis, its Applications and Computation) and by the Kazakhstan Mathematical Society The work is supported by the Nazarbayev University 091019CRP2109, PI Zhenisbek Assylbekov.

Keywords

asymptotic distribution
neural networks
partition function
word embeddings
word2vec

ASJC Scopus subject areas

General Mathematics

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10.32523/2077-9879-2022-13-4-70-81

Cite this

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abstract = "We develop an asymptotic theory for the partition function of the word embeddings model word2vec. The proof involves a study of properties of matrices, their determinants and distributions of random normal vectors when their dimension tends to infinity. The conditions imposed are mild enough to cover practically important situations. The implication is that for any word i from a vocabulary W, the context vector ci is a reflection of the word vector wi in approximately half of the dimensions. This allows us to halve the number of trainable parameters in static word embedding models.",

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CONVERGENCE OF THE PARTITION FUNCTION IN THE STATIC WORD EMBEDDING MODEL

Abstract

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Keywords

ASJC Scopus subject areas

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