A mathematical property of the GloVe model is that the vector representation of any given word when it acts as a center word is mathematically equivalent to its vector representation when it acts as a context word.

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GloVe, short for Global Vectors, is an unsupervised learning model used to obtain dense vector representations for words. It captures global corpus statistics by modeling the ratios of probabilities from a global word-word co-occurrence matrix. By doing so, it incorporates both the advantages of global matrix factorization and the local context window methods (like word2vec) to effectively represent the linear substructures of the word vector space.

GloVe

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GloVe employs a squared loss function to fit precomputed global corpus statistics, such as word-word co-occurrence counts. This approach is chosen because cross-entropy loss is often suboptimal for measuring the difference between two probability distributions when dealing with an extremely large corpus.

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