Applying Machine Learning to Discourse Processing

AAAI 1998 Spring Symposium Series
Stanford University
March 23-25

Following success in using machine learning (ML) techniques in areas such as speech recognition, part-of-speech tagging, word sense disambiguation, and parsing, there has been an increasing interest in applying ML to discourse processing. To date, there has been work in using machine learning techniques such as inductive learning methods (decision trees), statistical learning methods (HMMs), neural networks, and genetic algorithms to a number of discourse problems, e.g., dialogue act prediction, cue word usage, anaphora resolution, initiative tracking, and discourse segmentation.

In this symposium, we would like to bring together researchers with an interest in exploring the potential contribution of ML to problems in discourse interpretation and generation. Our goal is provide an opportunity for discussions among researchers in natural language discourse and in machine learning to facilitate collaboration between the two groups. We are interested in addressing the following issues:

The tentative symposium format includes short tutorials on ML techniques, presentations of technical papers, as well as sessions for experience-sharing and discussion of the above issues.

  • October 21, 1997 -- Electronic submissions are due
  • October 24, 1997 -- Hardcopy submissions are due
  • November 14, 1997 -- Acceptance/rejection notices are mailed out
  • January 17, 1998 -- Camera-ready papers due
  • February 6, 1998 -- Invited participants registration deadline
  • February 27, 1998 -- Final (open) registration deadline
  • March 23-25, 1998 -- Spring Symposium Series, Stanford University

    Symposium Information

  • Call for Participation (ascii)
  • Submission Information
  • Accepted Papers
  • Symposium Schedule (HTML with links to papers)
  • Symposium Schedule (postscript)
  • Working Notes Table of Contents
  • Information for authors of accepted papers
  • AAAI
  • AAAI 1998 Spring Symposium Series

  • Program Committee

  • Jennifer Chu-Carroll (co-chair) Bell Laboratories ( )
  • Nancy Green (co-chair), Carnegie Mellon University (
  • Barbara Di Eugenio , University of Pittsburgh
  • Peter Heeman, Oregon Graduate Institute
  • Diane Litman, AT&T Laboratories Research
  • Raymond Mooney , University of Texas
  • Johanna Moore, University of Pittsburgh
  • David Powers , Flinders University

  • Related Links

  • Discourse Resource Initiative
  • UCI: Information Related to Machine Learning