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ACL
2012

Historical Analysis of Legal Opinions with a Sparse Mixed-Effects Latent Variable Model

7 years 6 months ago
Historical Analysis of Legal Opinions with a Sparse Mixed-Effects Latent Variable Model
We propose a latent variable model to enhance historical analysis of large corpora. This work extends prior work in topic modelling by incorporating metadata, and the interactions between the components in metadata, in a general way. To test this, we collect a corpus of slavery-related United States property law judgements sampled from the years 1730 to 1866. We study the language use in these legal cases, with a special focus on shifts in opinions on controversial topics across different regions. Because this is a longitudinal data set, we are also interested in understanding how these opinions change over the course of decades. We show that the joint learning scheme of our sparse mixed-effects model improves on other state-of-the-art generative and discriminative models on the region and time period identiļ¬cation tasks. Experiments show that our sparse mixed-effects model is more accurate quantitatively and qualitatively interesting, and that these improvements are robust across d...
William Yang Wang, Elijah Mayfield, Suresh Naidu,
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors William Yang Wang, Elijah Mayfield, Suresh Naidu, Jeremiah Dittmar
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