Research

My main area of research is Natural Language Processing (NLP), and its application to human-computer interaction, educational technology, human-robot interaction, and multimedia systems. My goal is to use NLP to support both education and instruction, and collaboration between human or artificial agents. The theoretical aspects of my research concern the linguistic analysis, and the knowledge representation and reasoning that support the understanding and generation of NL discourse and dialogue. All my research has its empirical foundations in both qualitative and quantitative corpus analysis, including data mining techniques.

Director: Dr. Barbara Di Eugenio

What follows is a high-level description of our research. Please see the NLP lab web site for updated project descriptions and publications .

Research in Natural Language Processing (NLP) at UIC focuses on semantics, and discourse and dialogue processing. Our goal is to use NLP to support both education and instruction, and collaboration between human or artificial agents (for those readers who are not familiar with NLP, NLP studies the computational models that underlie the processing of human languages, and develops key technology that makes it possible for users to interact with a computer system using English, Italian or Japanese rather than a programming language).

Our group focuses on the computational modeling of extended text (discourse) and conversations between two or more agents (dialogue). The theoretical aspects of our research concern the linguistic analysis, and the knowledge representation and reasoning that support the understanding and generation of NL discourse and dialogue. The intended applications range from automatically producing instructional manuals (e.g., those that accompany any piece of equipment such as a stereo), to providing dialogue capabilities for Intelligent Tutoring Systems (ITSs), computer based tutors that can help students master a subject. The methodology we employ blends empirical and symbolic approaches, and consists of: data mining from text corpora; development of computational frameworks based on the information extracted from the corpus; and rigorous evaluation of the computational models via user studies.


Our major areas of interest right now are:

  • NLP for Educational Technology (previously supported by the Office of Naval Research [2000-2008], NSF [2005-2009], now by the Qatar National Research Foundation [2012-2015]). This work concerns building Intelligent Tutoring Systems and other educational technology interactive systems that can participate in a dialogue with their users.

  • Discourse Parsing (supported from 2002-2008 by an NSF CAREER award). We employ a novel methodology that couples a corpus parsed to obtain rich semantic representations and annotated with discourse relations to learn a first order model for discourse relations via inductive logic programming.

  • Human-Robot Interaction (supported by NSF, 2009-2013). We study human-robot interaction with the ultimate goal of building assistive robots for the elderly.
We are also active in other areas of research, including:

  • Empirical methods in discourse: tagging, statistical corpus analysis, machine learning. For example, we study coefficients of intercoder reliability ( CL squib on Kappa ) and work on inferring dialogue acts via extensions to Latent Semantic Analysis (ACL04 paper) and discourse relations.

  • Summarization for recommender systems and for health applications
Collaborators, past and present:
  • Davide Fossati, an alumnus of the NLP lab at UIC, collaborates with us on research on educational technology.
  • Stellan Ohlsson, UIC (Psychology), collaborated with us on cognitive models of tutorial dialogues.
  • Milos Zefran, UIC (ECE) and Jezekiel Ben-Arie UIC (ECE), collaborate with us on human-robot interaction
  • Andrew Boyd, UIC, Gail Keenan, UIC, Yives Lussier, UIC, collaborate with us on patient-centered summarization
  • Massimo Poesio, senior lecturer at the University of Essex, UK, worked with us on theories of referential expressions
  • Pam Jordan and Sandra Katz, research associates at the University of Pittsburgh, worked with us on analysis and modelling of peer learning.
  • Michael Glass Michael was a postdoctoral fellow from August 2000 to August 2002. He is now an Associate Professor at Valparaiso University.
 
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