This chapter explores the modeling of semantic space through the use of word embeddings produced by neural network models. It traces the development and potential use of this technique through the critical digital humanities methods proposed in the previous chapters. The author proposes strategies for using these vector space models in historical research through the use of models to align and combine multiple models.
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