Editor’s Choice: Vector Space Models for the Digital Humanities

Recent advances in vector-space representations of vocabularies have created an extremely interesting set of opportunities for digital humanists. These models, known collectively as word embedding models, may hold nearly as many possibilities for digital humanitists modeling texts as do topic models. Yet although they’re gaining some headway, they remain far less used than other methods (such as modeling a text as a network of words based on co-occurrence) that have considerably less flexibility.

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This content was selected for Digital Humanities Now by Editor-in-Chief Joshua Catalano based on nominations by Editors-at-Large: Antonio Jimenez-Munoz, Caitlin Christian-Lamb, Julia Bourbois, Rebecca Napolitano, Nathaniel Deines, Jennifer Rogers, Danae Tapia, Leigh Bonds, Covadonga Lamar, John Matson, and Vanessa Stone