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Any word embeddings, because they are trained on human input, will have biases. That’s true because no human is free from bias, so any machine learning model trained on that human input will have those biases. We must be aware of those biases in language processing.
This post explores how bias can creep into word embeddings like word2vec, and I thought it might make it more fun (for me, at least) if I analyze a model trained on what you, my readers...
The academic search engine’s policy on name changes is out of step with other search tools and publishers.