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Found 4 papers, 1 papers with code
The effects of gender bias in word embeddings on depression prediction
We use contextual and non-contextual embeddings that are trained on domain-independent as well as clinical domain-specific data.
Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories
Clinical word embeddings are extensively used in various Bio-NLP problems as a state-of-the-art feature vector representation.
Fact sheet: Automatic Self-Reported Personality Recognition Track
We propose an informed baseline to help disentangle the various contextual factors of influence in this type of case studies.
BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain
A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods.
Ranked #8 on
Sentence Embeddings For Biomedical Texts
on BIOSSES
(using extra training data)
