News: The Human Race to the Future is now available!
Courses
IFSC 2200 Ethics in the Profession
IFSC 4301 Information, Computing and the Future
IFSC 7101 Research Methodology
IFSC 7102 Research Tools
IFSC 7103 Research Applications
IFSC 7321 Information Science and Theory
BINF 4445/5445 Bioinformatics Theory and Applications
IFSC 7310 Information Systems Analysis
Upon request: Information Retrieval Tutorial
IFSC 3360 Systems Analysis and Design
IFSC 1305 Problem Solving Techniques IFSC
IFSC 4396 Information Systems Application (capstone project course)
Java course
MAILING ADDRESS: Department of Information ScienceUniversity of Arkansas at Little Rock EIT Building 2801 South University Avenue Little Rock , Arkansas 72204 email: jdberleant@ualr.edu
phone: (501) 569-3488
Fax: (501) 569-7049 Our group focuses on two main areas. In text mining, our work in recent years has focused on text empirics, an approach to determining and applying the characteristics of short text passages, such as sentences, that support increased automation in information extraction. In a recent paper we report progress on extracting interactions between molecules in the biological literature. We are currently polishing a paper on the next step. This is extracting not just whether molecules interact, but the type of interaction, as stated by a third term appearing in a relevant sentence. This is based on Lifeng Zhang's dissertation. We are also pursuing a recent line of research on automatic extraction of numerical information about constituents of agriculturally important plants from the literature, an ongoing project involving collaborators and external funding. This type of work requires reasoning under uncertainty, which is supported by research in probability, described next.
Probability research in the Berleant lab emphasizes the theory and application of manipulating imprecise probabilities. These are interval-valued ranges that safely bound an unknown point probability value. An important aspect of that is dealing with cumulative distributions that safely bound an unknown specific cumulative distribution curve. An algebra of such data objects is derivable by first modeling the problem using Dempster-Shafer theory. We have published advances in using this concept in financial analysis, to model physical systems, for reliability analysis, in incorporating information about correlations between imprecisely defined distributions, and other applications including text mining and information quality.
Bringing the excitement of research to a wider audience is important in facilitating the general public’s understanding and appreciation of scholarship. I am doing that with a manuscript that has been drafted on technology foresight, i.e., possible futures.
This book is intended for the general public and emphasizes computing and biotechnology, although not exclusively.
The current draft is free upon request by email and comments are invited.
Here is information on pubs, students, and funding.