Undergraduate Research Project

Contact Information

Student:              Jordanna Chord- Homepage

                            Senior, Computer Science & Entrepreneurial Leadership

                            Gonzaga University, Spokane, WA


Email:                 jchord@gonzaga.edu


Project Name:    Author Identification Challenge

Faculty Advisor: Dr. Paul B. Kantor, Professor II
                            School of Communication, Information, and Library Studies
                            Rutgers University

Project Summary

Overall Summary

Alexander Genkin, David D. Lewis, and David Madigan have developed software that implements Bayesian logistic regression with two choices of priors: Gaussian and Laplace (also known as double exponential). A detailed technical report presenting theoretical background on the approach, the fitting algorithm, and experimental results can be found at http://stat.rutgers.edu/~madigan/PAPERS/shortFat-v13.ps .

This tool allows us to take a set of lexical data in which we can identify and create a model training file.  This model training file can be applied to lexical data that we are attempting to identify and determine whether they are part of the same class.

For example, past research has been done on un-ambiguating Federalist Papers that was either written by Alexander Hamilton, James Madison by using word frequencies in the training model.  We are able to determine the probability that a test document is of one class, Hamilton, or the other, Madison.

KDD Challenge

As part of the ongoing effort to improve and expand the BBR project, this research will include participation in a KDD challenge.  Provided data from the BioSci database, we will run numerous tests to distinguish between authors who share the same name.  Experimenting with a combination of data that will be provided for each document, we hope to be able to distinguish between authors whom can't be distinguished by name.  Studies will include the use of various factors including co-authors, keywords, abstracts' lexicon, the address of the lead author, and the scientific journal classification.


Entity Resolution for Authors of Biological Sciences Papers
Jordanna Chord, Gonzaga University, Spokane, WA
Melissa Mitchell, University of Detroit - Mercy, Detroit, MI
Mentor: Dr. Paul Kantor, SCILS
When several persons have the same name, we would like to be able to tell them apart, by characteristics of their writings. The intelligence community, which supports our work, has defined a "challenge problem" which approximates the real problem that they face. They will present many items and ask us to separate them into those authored by "different persons".
To prepare for this challenge problem, we have selected ten "author names" for which there is one prolific contributor, and approximately an equal number of papers by other (different) persons. We drew associated abstracts address information and keywords from the online database Pubmed.
We applied Bayesian Binary Regression to identify author using a combination of seven document attributes. We have achieved strong performance (defined as a Receiver Operating Characteristic with an area under the curve of 80% or better, in several ways. This can be done using only keywords, or only addresses, or with a representation that included all attributes: abstract words, address words, address fields, co-authors, keywords, and title.
Results clearly identified keywords and addresses as working well alone in identifying documents. One expects the use of all variables to be better. But there is little difference. This is interpreted as meaning that either set of variables can accomplish the classification, but that they do not complement each other. Future research will examine how the regression approach balances selection among them when all variables are included.
Thanks to: Alex Genkin, Dmitriy Fradkin and Andrei Anghelescu for crucial support with coding and the use of the BBR software.

Other Applications

The ability to classify documents is an important tool in various industries.


Work in progress. Daily Journal. Also contains final results.

Links of Importance

BBR: Bayesian Logistic Regression Software