Abstract

The vast amounts of on-line text nowavailable 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. 1. Introduction An information extraction #IE# sys...

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