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. …

Hands-On Nlp For An Interdisciplinary Audience, Elizabeth D. Liddy, Nancy Mccracken

School of Information Studies - Faculty Scholarship

The need for a single NLP offering for a diverse mix of graduate students (including computer scientists, information scientists, and linguists) has motivated us to develop a course that provides students with a breadth of understanding of the scope of real world applications, as well as depth of knowledge of the computational techniques on which to build in later experiences. We describe the three hands-on tasks for the course that have proven successful, namely: 1) in-class group simulations of computational processes; 2) team posters and public presentations on state-of-the-art commercial NLP applications, and; 3) team projects implementing various levels of …

Discerning Emotions In Texts, Victoria L. Rubin, Jeffrey M. Stanton, Elizabeth D. Liddy

School of Information Studies - Faculty Scholarship

We present an empirically verified model of discernable emotions, Watson and Tellegen’s Circumplex Theory of Affect from social and personality psychology, and suggest its usefulness in NLP as a potential model for an automation of an eight-fold categorization of emotions in written English texts. We developed a data collection tool based on the model, collected 287 responses from 110 non-expert informants based on 50 emotional excerpts (min=12, max=348, average=86 words), and analyzed the inter-coder agreement per category and per strength of ratings per sub-category. The respondents achieved an average 70.7% agreement in the most commonly identified emotion categories per text. …

What Do You Mean? Finding Answers To Complex Questions, Anne R. Diekema, Ozgur Yilmazel, Jiangping Chen, Sarah Harwell, Elizabeth D. Liddy, Lan He

School of Information Studies - Faculty Scholarship

This paper illustrates ongoing research and issues faced when dealing with real-time questions in the domain of Reusable Launch Vehicles (aerospace engineering). The question- answering system described in this paper is used in a collaborative learning environment with real users and live questions. The paper describes an analysis of these more complex questions as well as research to include the user in the question-answering process by implementing a question negotiation module based on the traditional reference interview.

A Breadth Of Nlp Applications, Elizabeth D. Liddy

School of Information Studies - Faculty Scholarship

The Center for Natural Language Processing (CNLP) was founded in September 1999 in the School of Information Studies, the “Original Information School”, at Syracuse University. CNLP’s mission is to advance the development of human-like, language understanding software capabilities for government, commercial, and consumer applications. The Center conducts both basic and applied research, building on its recognized capabilities in Natural Language Processing. The Center’s seventeen employees are a mix of doctoral students in information science or computer engineering, software engineers, linguistic analysts, and research engineers.

Natural Language Processing, Elizabeth D. Liddy

School of Information Studies - Faculty Scholarship

Natural Language Processing (NLP) is the computerized approach to analyzing text that is based on both a set of theories and a set of technologies. And, being a very active area of research and development, there is not a single agreed-upon definition that would satisfy everyone, but there are some aspects, which would be part of any knowledgeable person’s definition.

An Nlp Approach For Improving Access To Statistical Information For The Masses, Elizabeth D. Liddy, Jennifer H. Liddy

School of Information Studies - Faculty Scholarship

Naïve users need to access statistical information, but frequently do not have the sophisticated levels of understanding required in order to translate their information needs into the structure and vocabulary of sites which currently provide access to statistical information. However, these users can articulate quite straightforwardly in their own terms what they are looking for. One approach to satisfying the masses of citizens with needs for statistical information is to automatically map their natural language expressions of their information needs into the metadata structure and terminology that defines and describes the content of statistical tables. To accomplish this goal, we …

Searching And Search Engines: When Is Current Research Going To Lead To Major Progress?, Elizabeth D. Liddy

School of Information Studies - Faculty Scholarship

For many years, users of commercial search engines have been hearing how the latest in information and computer science research is going to improve the quality of the engines they rely on for fulfilling their daily information needs. However, despite what is heard, these promises have not been fulfilled. While the Internet has dramatically increased the amount of information to which users now have access, the key issue appears to be unresolved – the results for substantive queries are not improving. However, the past need not predict the future because sophisticated advances in Natural Language Processing (NLP) have, in fact, …