[gov2001-l] Abstract

Ranking Documents with Wordscores In recent years, corporate juggernauts such as Google, have amassed enormous fortunes by making use of relatively simple document processing algorithms. A huge body of literature has sprung forth detailing techniques for information retrieval, document ranking and classification of large sequences and vectors, and countless algorithms have been developed to take advantage rapid developments in this ever-changing realm. One such algorithm is Wordscores, a simple document ranking algorithm recently developed by Michael Laver, Kenneth Benoit and John Garry for extracting relative political policy positions from documents. Despite its simplicity Wordscores performs remarkably well at ranking political documents when compared to a variety of classic document ranking algorithms. In this paper, we examine the performance of Wordscores and illustrate its superiority at ranking political texts, proposing extensions for improving its performance, detailing its assumptions, and specifying conditions which must be met in order to use it to make substantiative claims.