About the Project
We spent our term replicating the research conducted in the 2016 paper produced by Bolukbasi et al. titled, “Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings”, where that team aimed to expose the implicit gender biases found in the way that we use our language through word embeddings and to also determine whether a soft or hard debiasing algorithm would be more effective in eliminating that gender bias within word embeddings.
What's a Word Embedding?
An object for text analysis and text generation through mapping text into individual word vectors. Machine learning allows relationships between words and their surrounding text to be highlighted. Applications for word embeddings include consumer feedback parsing, spam detection, and information retrieval(ex. search engines). For our purposes, we use vector mathematics to expose biased relationships between words unrecognized or unproven through other forms of text analysis. We will specifically focus on the gender bias.