Static Word Embeddings

# GloVe Embeddings

Introduced by Pennington et al. in GloVe: Global Vectors for Word Representation

GloVe Embeddings are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:

$$J=\sum_{i, j=1}^{V}f\left(𝑋_{i j}\right)(w^{T}_{i}\tilde{w}_{j} + b_{i} + \tilde{b}_{j} - \log{𝑋}_{ij})^{2}$$

where $w_{i}$ and $b_{i}$ are the word vector and bias respectively of word $i$, $\tilde{w}_{j}$ and $b_{j}$ are the context word vector and bias respectively of word $j$, $X_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.

#### Papers

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