Sean Gerrish

NeurIPS Conference 2012 Conference Paper

How They Vote: Issue-Adjusted Models of Legislative Behavior

• Sean Gerrish
• David Blei

We develop a probabilistic model of legislative data that uses the text of the bills to uncover lawmakers' positions on specific political issues. Our model can be used to explore how a lawmaker's voting patterns deviate from what is expected and how that deviation depends on what is being voted on. We derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we demonstrate both improvement in heldout predictive performance and the model's utility in interpreting an inherently multi-dimensional space.

NeurIPS Conference 2012 Conference Paper

Scalable Inference of Overlapping Communities

• Prem Gopalan
• Sean Gerrish
• Michael Freedman
• David Blei
• David Mimno

We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel. It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60, 000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of communities in large real-world networks, and detects the true communities in 280 benchmark networks with equal or better accuracy compared to other scalable algorithms.

ICML Conference 2011 Conference Paper

Predicting Legislative Roll Calls from Text

• Sean Gerrish
• David M. Blei

ICML Conference 2010 Conference Paper

A Language-based Approach to Measuring Scholarly Impact

• Sean Gerrish
• David M. Blei

Identifying the most influential documents in a corpus is an important problem in many fields, from information science and historiography to text summarization and news aggregation. Unfortunately, traditional bibliometrics such as citations are often not available. We propose using changes in the thematic content of documents over time to measure the importance of individual documents within the collection. We describe a dynamic topic model for both quantifying and qualifying the impact of these documents. We validate the model by analyzing three large corpora of scientific articles. Our measurement of a document’s impact correlates significantly with its number of citations.

NeurIPS Conference 2009 Conference Paper

Reading Tea Leaves: How Humans Interpret Topic Models

• Jonathan Chang
• Sean Gerrish
• Chong Wang
• Jordan Boyd-Graber
• David Blei

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models, summarize the corpus, and guide exploration of its contents. However, whether the latent space is interpretable is in need of quantitative evaluation. In this paper, we present new quantitative methods for measuring semantic meaning in inferred topics. We back these measures with large-scale user studies, showing that they capture aspects of the model that are undetected by previous measures of model quality based on held-out likelihood. Surprisingly, topic models which perform better on held-out likelihood may infer less semantically meaningful topics.