Project GenIE

Sponsor: National Science Foundation (Start Date: June 1, 2002; End Date with no-cost extension: May 31, 2010)

Award Title: CAREER: Intelligent Argument Generation in Text and Information Graphics  (Award Number 0132821).

Summary: Lay people are faced with increasing responsibility for making vital decisions based on technical arguments, often involving quantitative and statistical data. The goal of this type of argument is to explain the reasoning and evidence leading to some conclusion, so that others may evaluate its degree of plausibility and certainty.   However, lay people may have trouble understanding and evaluating arguments based upon probability and statistics.  Furthermore, for conciseness, supporting data is often presented graphically, e.g. in line graphs and bar charts.  However, it is debatable whether people without technical training are able to take full advantage of data graphics without additional assistance.  To complicate this problem, the data underlying an argument may change as new information becomes available, requiring a new argument to be communicated. This research will address these problems by developing techniques for implementing computer systems to communicate technical arguments.  The arguments will be communicated using integrated text and data graphics. 

Although the research is applicable to many different fields, for testing purposes this project will implement the techniques in a prototype system for use by genetics counselors. In current practice, a counselor meets with a client to perform a risk analysis for genetic disorders and to discuss the results; afterwards, the counselor writes a brief summary letter about the client's case for the client to refer to later.  The prototype system will be designed to automatically produce a hypertext summary presentation based upon information provided by the counselor, but expanded by the system, e.g., to include references to relevant scientific data and methods.  Using a combination of text and graphics, the hypertext presentation will be designed for a non-technical audience.

The project will integrate empirical methods from computational linguistics and human-computer interaction.  Empirical methods are necessary to determine appropriate techniques for conveying technical arguments to people with non-technical backgrounds.  Initially, the project will developanalytical models based upon a corpus of human-authored multimedia presentations.  For example, corpus studies might determine which features of arguments suggest where supporting graphics should be placed.  Next, human-computer interaction experiments will be used to determine the effectiveness of techniques based upon the analytical models; and to investigate alternatives to the techniques that are shown not to be sufficient.  The resulting empirically-based techniques will be incorporated into computational models.  The computational models will extend current approaches in natural language generation, automated graphic design, and intelligent multimedia presentation systems.  Although the computational models will be demonstrated within a prototype system for genetics counseling patient education, the models could be targeted for other technical domains and uses.

Other Funding: UNCG New Faculty Grant (Spring 2000), UNCG Summer Excellence Research Grants (2000, 2001), UNCG Undergraduate Research Assistantships (Fall 2002- Spring 2003, Fall 2004), DOE Graduate Research Assistantship (Fall 2002)

• Artificial Intelligence: knowledge representation for multimedia generation and biomedical applications (clinical genetics)
• Natural (Human) Language Generation : computational analysis/generation of argumentation; multimedia document generation; text mining and content analysis
• User Modeling/Human Computer Interaction/Cognitive Modeling: human multimedia processing; communication of scientific information to lay audiences.

• Coding Manual for corpus annotation.
• Nancy Green.  An Empirical Study of Multimedia Argumentation.  Proceedings of the International Conference on Computational Systems, Workshop on Computational Models of Natural Language Arguments (CMNA 2001), May 2001. [Available as Springer Lecture Notes in Computer Science 2073, pp. 1009-18.]
• Nancy Green. Towards an Empirical Model of Argumentation in Medical Genetics. Workshop Papers: Computer Models of Natural Argumentation (CMNA 2003), workshop of International Joint Conference on Artificial Intelligence (IJCAI-03), Aug. 9, 2003, Acapulco, Mexico, p. 39-44.
• Nancy Green. Using HCI Experiments to Validate Intelligent Multimedia Cue Generation. Proceedings of Florida Artificial Intelligence Research Symposium, FLAIRS 2004.
• Nancy Green. Analysis of Linguistic Features Associated with Point of View for Generating Stylistically Appropriate Text. Papers from the 2004 AAAI Spring Symposium: Exploring Attitude and Affect in Text: Theories and Applications. AAAI Press. (Extended version in Shanahan, Qu, and Wiebe (eds), Computing Attitude and Affect in Text: Theory and Applications. Spring, 2005, 33-40.)
• N. Green, T. Britt, and K. Jirak. Communication of Uncertainty in Clinical Genetics Patient Health Communication Systems. AAAI 2004 Fall Symposium on Dialogue Systems for Health Communication. 
• N. Green. Graphics for Patient-Tailored Information in Clinical Genetics. Papers from 2005 AAAI Spring Symposium: Reasoning with Mental and External Diagrams.
  
• N. Green. A Bayesian Network Coding Scheme for Annotating Biomedical Information Presented to Genetic Counseling Clients. Journal of Biomedical Informatics 38 (2005) 130-144.
• N. Green. Affective Factors in Generation of Tailored Genomic Information.  User Modeling 2005, Workshop on Adapting the Interaction Style to Affective Factors, July 2005, Edinburgh.
• N. Green, T. Britt, K. Jirak, D. Waizenegger, and X. Xin. User Modeling for Tailored Genomic E-health Information. User Modeling 2005, Workshop on Personalisation for eHealth, July 2005, Edinburgh. 
• N. Green. Design of Information Graphics for Causal Arguments. Computer Models of Natural Argument (CMNA 2005), July 2005, Edinburgh.
• N. Green. Representing Normative Arguments in Genetic Counseling. Argumentation for Consumers of Healthcare: Papers from 2006 AAAI Spring Symposium.
• N. Green. Generation of Biomedical Arguments for Lay Readers. Proc. of Int. Conf. on Natural Language Generation (INLG 2006).
• N. Green. A Study of Argumentation in a Causal Probabilistic Domain: Genetic Counseling. Int. J. of Intelligent Systems 22(1), Jan. 2007, 71-93.
• N. Green. Dialectical Argumentation in Causal Domains. CMNA 2008, 8th Int. Workshop on Computational Models of Natural Argument, 31-38.
  
• N. Green. Analysis of Communication of Uncertainty in Genetic Counseling Patient Letters for Design of a Natural Language Generation System. Social Semiotics 20(1), 2010, 77-86.
• N. Green. Representation of Argumentation in Text with Rhetorical Structure Theory. Argumentation. 24(2), 2010, 181-196.
• N. Green. Towards Intelligent Learning Environments for Scientific Argumentation. Proc. ITS 2010 Workshop on Intelligent Tutoring Technologies for Ill-Defined Problems and Ill-Defined Domains, Pittsburgh, PA, June 18, 2010.
  
• N. Green, R. Dwight, K. Navoraphan, and B. Stadler. Natural Language Generation of Transparent Arguments for Lay Audiences. Argument and Computation. To appear (2010-11).
• Presentation
  

• autosomal recessively inherited hearing loss
  

Computer Science Undergraduate Research Assistants: 

Darryl Keeter
Carolyn McCann
Carmen Navarro-Luzon
Timothy O'Dell
Zach Todd
David Waizenegger

UNCG Center for Biotechnology, Genomics, and Health Research
Students and Faculty of the UNCG Master of Science Program in Genetic Counseling