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Distributed word embeddings such as Word2Vec and GloVe have been widelyadopted in industrial context settings. Major technical applications of GloVeinclude recommender systems and natural language processing. The fundamentaltheory behind GloVe relies on the selection of a weighting function in theweighted least squres formulation that computes the powered ratio of wordoccurrence count and the maximum word count in the corpus. However, the initialformulation of GloVe is not theoretically sound in two aspects, namely theselection of the weighting function and its power exponent is ad-hoc. In thispaper, we utilize the theory of extreme value analysis and propose atheoretically accurate version of GloVe. By reformulating the weighted leastsquares loss function as the expected loss function and accurately choosing thepower exponent, we create a theoretically accurate version of GloVe. Wedemonstrate the competitiveness of our algorithm and show that the initialformulation of GloVe with the suggested optimal parameter can be viewed as aspecial case of our paradigm.

Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference