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
A full list of publications is available here .
Our major areas of interest right now are:
Computational models of tutorial dialogue (supported by the Office of Naval Research). This work concerns building ITSs that can participate in a dialogue with their users. The project DIAG-NLP has been one of the first to show that a language interface to an ITS does engender more learning in students (please see the following two papers, AIED05, and ACL05). Currently, we are exploring what distinguishes expert from novice tutors, in order to model the more effective tutors in an interface to an ITS that tutors students on basic data structures and algorithms. Read more
Lexical semantics and inductive logic programming to learn discourse relations and domain knowledge (supported by an NSF CAREER award). We employ a novel methodology that couples a corpus parsed to obtain rich semantic representations (HLT-NAACL03) and annotated with discourse relations to learn a first order model for discourse relations via inductive logic programming. The final goal is to (semi)automatically acquire domain knowledge about action verbs and rhetorical knowledge about how those actions are related in instructional discourse. Read more
Modeling collaboration in human-human and computer-human dialogues (supported by NSF, Advanced Learning Technologies program). We are extending our model of commitment in dialogue, developed under the project Coconut project at the University of Pittsburgh (see IJHCS 00, [.ps.gz]). We are modeling peer interactions in learning, and developing a peer dialogue agent in the domain of basic data structures and algorithms. Read more
We are also active in other areas of research, including: