Library and Information Science Commons^™

Collecting Legacy Corpora From Social Science Research For Text Mining Evaluation, Bei Yu, Min-Chun Ku

School of Information Studies - Faculty Scholarship

In this poster we describe a pilot study of searching social science literature for legacy corpora to evaluate text mining algorithms. The new emerging field of computational social science demands large amount of social science data to train and evaluate computational models. We argue that the legacy corpora that were annotated by social science researchers through traditional Qualitative Data Analysis (QDA) are ideal data sets to evaluate text mining methods, such as text categorization and clustering. As a pilot study, we searched articles that involve content analysis and discourse analysis in leading communication journals, and then contacted the authors regarding …

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 …

Questions To Be Asked & Answered On Nlp’S Role In Improving Semantic Annotation For Ir, Elizabeth D. Liddy

School of Information Studies - Faculty Scholarship

What was early information retrieval like? (before it was called search!)

How was NLP first applied to the task?

Which levels of language analysis were utilized?

Which were successful? Which were not?

Why were other levels not incorporated ?

Do we now see that the higher levels can and need to be included?

If they are, how might they change how we do IR, as well as what tasks we use it for?

Supporting Inquiry By Identifying Gaps In Student Confidence: Development Of A Measure Of Perceived Competence, Marilyn P. Arnone, Ruth V. Small, Rebecca Reynolds

School of Information Studies - Faculty Scholarship

Critical to inquiry-based learning is information literacy. Educators can enhance students’ experiences during the inquiry process if they are aware of the skill areas in which students either have or lack confidence. This article describes the development and psychometric properties of the Perceived Competence in Information Skills (PCIS) measure. Educators can use the measure to support student inquiry by identifying and addressing gaps in student confidence. The measure is freely available through Syracuse University’s Center for Digital Literacy.