  We analyze bias in historical corpora as encoded in diachronic distributional
semantic models by focusing on two specific forms of bias, namely a political
(i.e., anti-communism) and racist (i.e., antisemitism) one. For this, we use a
new corpus of German parliamentary proceedings, DeuPARL, spanning the period
1867--2020. We complement this analysis of historical biases in diachronic word
embeddings with a novel measure of bias on the basis of term co-occurrences and
graph-based label propagation. The results of our bias measurements align with
commonly perceived historical trends of antisemitic and anti-communist biases
in German politics in different time periods, thus indicating the viability of
analyzing historical bias trends using semantic spaces induced from historical
corpora.
