5 citations · 8 across the 3 of their papers we have counts for
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cs.CL2022★ 1 cited
The effects of gender bias in word embeddings on depression prediction
Gizem Sogancioglu, Heysem Kaya
Word embeddings are extensively used in various NLP problems as a state-of-the-art semantic feature vector representation. Despite their success on various tasks and domains, they…
cs.CL2022★ 5 cited
Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories
Gizem Sogancioglu, Fabian Mijsters, Amar van Uden +1
Clinical word embeddings are extensively used in various Bio-NLP problems as a state-of-the-art feature vector representation. Although they are quite successful at the semantic re…