AI Seminar
    -------------------------------
    Tuesday, March 9th, 2004
    4:00 pm - 5:30 pm
    175 ATL (Large Conference Room)

    "Combining Labeled and Unlabeled Data for Learning Cross-document
     Structural Relationships"

    Zhu Zhang
    Department of Electrical Engineering and Computer Science
    and School of Information
    University of Michigan




----------------------------------
Multi-document discourse analysis has emerged with the potential of
improving various NLP applications. Based on the newly proposed
Cross-document Structure Theory (CST), we describe an empirical study that
classifies CST relationships between sentence pairs extracted from
topically related documents, exploiting both labeled and unlabeled
data. We investigate a binary classifier for determining existence of
structural relationships and a full classifier using the full taxonomy of
relationships. We show that in both cases the exploitation of unlabeled
data helps improve the performance of learned classifiers.