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Text corpora are widely used resources for measuring societal biases andstereotypes. The common approach to measuring such biases using a corpus is bycalculating the similarities between the embedding vector of a word (likenurse) and the vectors of the representative words of the concepts of interest(such as genders). In this study, we show that, depending on what one aims toquantify as bias, this commonly-used approach can introduce non-relevantconcepts into bias measurement. We propose an alternative approach to biasmeasurement utilizing the smoothed first-order co-occurrence relations betweenthe word and the representative concept words, which we derive byreconstructing the co-occurrence estimates inherent in word embedding models.We compare these approaches by conducting several experiments on the scenarioof measuring gender bias of occupational words, according to an EnglishWikipedia corpus. Our experiments show higher correlations of the measuredgender bias with the actual gender bias statistics of the U.S. job market - ontwo collections and with a variety of word embedding models - using thefirst-order approach in comparison with the vector similarity-based approaches.The first-order approach also suggests a more severe bias towards female in afew specific occupations than the other approaches.

Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence