Research

Computational Social Science

Computational social science involves the use of computational methods and models to leverage <A HREF="http://www.sciencemag.org/cgi/content/summary/323/5915/721">"the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors"</A>. Research in the CLIP Laboratory is at the forefront of this emerging area, and includes sentiment analysis (computational modeling and prediction of opinions, perspective, and other private states), automatic analysis and visualization of the scientific literature, modeling the diffusion of technological innovations, and modeling and prediction of social goals and actions such as persuasion. Primary lab faculty involved in computational social science include <A HREF="URL">Jordan Boyd-Graber</A> (scientific literature analysis, persuasion),<A HREF="URL">Bonnie Dorr</A> (sentiment analysis, scientific literature analysis), <A HREF="URL">Jimmy Lin</A> (scientific literature analysis), <A HREF="URL">Doug Oard</A> (technological innovation), <A HREF="http://umiacs.umd.edu/~resnik/">Philip Resnik</A> (sentiment, persuasion), and <A HREF="URL">Amy Weinberg</A> (sentiment, persuasion).

Information Retrieval: From Tweets to Tomes

Faculty	Jimmy Lin Doug Oard
Postdocs
Graduate Students
The goal of information retrieval is to help people find what they are looking for. Information retrieval research in the CLIP lab focuses principally on retrieval based on the language contained in text, in speech, and in document images. We work across a broad range of content types, from tweets to tomes, from talking to texting, and from Cebuano to Chinese. Three perspectives inform our work: we integrate a broad range of computational linguistics techniques, we focus on scalable techniques that can accommodate very large collections we sometimes draw the boundaries of our “systems” very broadly to include both the automated tools that we create and the process by which users can best employ those tools. One example that illustrates these perspectives is our work with “cross-language information retrieval,” in which close coupling of machine translation and information retrieval techniques make it possible for people to find and use information written in languages that they can neither read nor write. Another example is our work on the design and evaluation of “question answering” systems that can automatically find and present answers to complex questions, which serves as a bridge between our work on information retrieval and summarization. Representative Publications and Project Pages: Publication 1 Publication 2