Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
Nay, J. (2016). “Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text.” Proceedings of 2016 Empirical Methods in Natural Language Processing Workshop on NLP and Computational Social Science, 49–54, Association for Computational Linguistics.
6 Pages Posted: 21 Dec 2017 Last revised: 23 May 2022
Date Written: November 5, 2016
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 finegrained 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 vectorarithmetic-based investigations of complex relationships between word sources based on their texts. We are extending this to create a more comprehensive legal semantic map.
Keywords: artificial intelligence, machine learning, natural language processing, legal, policy, computational law, AI alignment
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