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 identi es logical relations among information extracted from the text. Previous corpusbased 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 classi ers are needed, but even derives the feature set for each classi er automatically. Performance equals that of a partially trainable discourse module requiring manual customization for each domain.