Wrap-Up: a Trainable Discourse Module for Information Extraction
<|reference_start|>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.<|reference_end|>
@article{soderland1994wrap-up:,
    title={Wrap-Up: a Trainable Discourse Module for Information Extraction},
    author={S. Soderland, Lehnert. W},
    journal={Journal of Artificial Intelligence Research, Vol 2, (1994),
  131-158},
    year={1994},
    archivePrefix={arXiv},
    eprint={cs/9412101},
    primaryClass={cs.AI}
}