Wrap-Up: a Trainable Discourse Module for Information Extraction
  The vast amounts of on-line text now available have led to renewed interest
in information extraction (IE) systems that analyze unrestricted text,
producing a structured representation of selected information from the text.
This paper presents a novel approach that uses machine learning to acquire
knowledge for some of the higher level IE processing. Wrap-Up is a trainable IE
discourse component that makes intersentential inferences and identifies
logical relations among information extracted from the text. Previous
corpus-based approaches were limited to lower level processing such as
part-of-speech tagging, lexical disambiguation, and dictionary construction.
Wrap-Up is fully trainable, and not only automatically decides what classifiers
are needed, but even derives the feature set for each classifier automatically.
Performance equals that of a partially trainable discourse module requiring
manual customization for each domain.
