An Embedding Model for Predicting Roll-Call Votes

Peter Kraft · Hirsh Jain · Alexander M. Rush

34 citations0/0 files storedConference on Empirical Methods in Natural Language Processing (EMNLP)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Computational and Text Analysis Methods Electoral Systems and Political Participation Artificial Intelligence in Law

Abstract

We develop a novel embedding-based model for predicting legislative roll-call votes from bill text.The model introduces multidimensional ideal vectors for legislators as an alternative to single dimensional ideal point models for quantitatively analyzing roll-call data.These vectors are learned to correspond with pre-trained word embeddings which allows us to analyze which features in a bill text are most predictive of political support.Our model is quite simple, while at the same time allowing us to successfully predict legislator votes on specific bills with higher accuracy than past methods.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 34 citations exist upstream.

References

2004The Statistical Analysis of Roll Call Data 2011Predicting Legislative Roll Calls from Text 2012How They Vote: Issue-Adjusted Models of Legislative Behavior

Cited by

2018Reading China: Predicting Policy Change with Machine Learning 2020Understanding the Language of Political Agreement and Disagreement in Legislative Texts 2023UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language 2022Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts 2025Knowing our choices: unveiling true voting patterns through machine learning (ML) and natural language processing (NLP) in European Parliament 2025Political Actor Agent: Simulating Legislative System for Roll Call Votes Prediction with Large Language Models 2023Moral rhetoric in discrete choice models: a Natural Language Processing approach 2023Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.