Library and Information Science Commons^™

A Longitudinal Study Of Language And Ideology In Congress, Bei Yu, Daniel Diermeier

School of Information Studies - Faculty Scholarship

This paper presents an analysis of the legislative speech records from the 101st-108th U.S. Congresses using machine learning and natural language processing methods. We use word vectors to represent the speeches in both the Senate and the House, and then use text categorization methods to classify the speakers by their ideological positions. The classification accuracy indicates the level of distinction between the liberal and the conservative ideologies. Our experiment results demonstrate an increasing partisanship in the Congress between 1989 and 2006. Ideology classifiers trained on the House speeches can predict the Senators' ideological positions well (House-to-Senate prediction), however the Senate-to-House …

Certainty Identification In Texts: Categorization Model And Manual Tagging Results, Elizabeth D. Liddy, Victoria L. Rubin, Noriko Kando

School of Information Studies - Faculty Scholarship

This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions – level, perspective, focus, and time of certainty.

The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. …

Dr-Link: A System Update For Trec-2, Elizabeth D. Liddy, Sung H. Myaeng

School of Information Studies - Faculty Scholarship

The theoretical goal underlying the DR-LINK System is to represent and match documents and queries at the various linguistic levels at which human language conveys meaning. Accordingly, we have developed a modular system which processes and represents text at the lexical, syntactic, semantic, and discourse levels of language. In concert, these levels of processing permit DR-LINK to achieve a level of intelligent retrieval beyond more traditional approaches. In addition, the rich annotations to text produced by DR-LINK are replete with much of the semantics necessary for document extraction. The system was planned and developed in a modular fashion and functional …