Shunit Agmon
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View article: Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study
Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study Open
Background Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are models that enable rep…
View article: Generating Product Insights from Community Q&A
Generating Product Insights from Community Q&A Open
In e-commerce sites, customer questions on the product details-page express the customers' information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address th…
View article: Gender-sensitive word embeddings for healthcare
Gender-sensitive word embeddings for healthcare Open
Objective To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its per…