Work

An Embedding Model for Predicting Roll-Call Votes

Year: 2016

Type: conference-paper

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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d16-1221

Authors: Peter Kraft, Hirsh Jain, Alexander M. Rush

Institution: Harvard University

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

OpenAlex ID: W2563741301