  We compare policy differences across institutions by embedding
representations of the entire legal corpus of each institution and the
vocabulary shared across all corpora into a continuous vector space. We apply
our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and
official summaries of Congressional bills. The model discerns meaningful
differences between government branches. We also learn representations for more
fine-grained word sources: individual Presidents and (2-year) Congresses. The
similarities between learned representations of Congresses over time and
sitting Presidents are negatively correlated with the bill veto rate, and the
temporal ordering of Presidents and Congresses was implicitly learned from only
text. With the resulting vectors we answer questions such as: how does Obama
and the 113th House differ in addressing climate change and how does this vary
from environmental or economic perspectives? Our work illustrates
vector-arithmetic-based investigations of complex relationships between word
sources based on their texts. We are extending this to create a more
comprehensive legal semantic map.
